Epigenome-wide meta-analysis of BMI in nine cohorts: Examining the utility of epigenetically predicted BMI.

This study sought to examine the association between DNA methylation and body mass index (BMI) and the potential of BMI-associated cytosine-phosphate-guanine (CpG) sites to provide information about metabolic health. We pooled summary statistics from six trans-ethnic epigenome-wide association studies (EWASs) of BMI representing nine cohorts (n = 17,034), replicated these findings in the Women's Health Initiative (WHI, n = 4,822), and developed an epigenetic prediction score of BMI. In the pooled EWASs, 1,265 CpG sites were associated with BMI (p < 1E-7) and 1,238 replicated in the WHI (FDR < 0.05). We performed several stratified analyses to examine whether these associations differed between individuals of European and African descent, as defined by self-reported race/ethnicity. We found that five CpG sites had a significant interaction with BMI by race/ethnicity. To examine the utility of the significant CpG sites in predicting BMI, we used elastic net regression to predict log-normalized BMI in the WHI (80% training/20% testing). This model found that 397 sites could explain 32% of the variance in BMI in the WHI test set. Individuals whose methylome-predicted BMI overestimated their BMI (high epigenetic BMI) had significantly higher glucose and triglycerides and lower HDL cholesterol and LDL cholesterol compared to accurately predicted BMI. Individuals whose methylome-predicted BMI underestimated their BMI (low epigenetic BMI) had significantly higher HDL cholesterol and lower glucose and triglycerides. This study confirmed 553 and identified 685 CpG sites associated with BMI. Participants with high epigenetic BMI had poorer metabolic health, suggesting that the overestimation may be driven in part by cardiometabolic derangements characteristic of metabolic syndrome.

Epigenetic gestational age and the relationship with developmental milestones in early childhood.

Shorter gestational age (GA) is a risk factor of developmental delay. GA is usually estimated clinically from last menstrual period and ultrasound. DNA methylation (DNAm) estimates GA using sets of cytosine-guanine-sites coupled with a clock algorithm. Therefore, DNAm-estimated GA may better reflect biological maturation. A DNAm GA greater than clinical GA, known as gestational age acceleration (GAA), may indicate epigenetic maturity and holds potential as an early biomarker for developmental delay risk. We used data from the Upstate KIDS Study to examine associations of DNAm GA and developmental delay within the first 3 years based on the Ages & Stages Questionnaire® (n = 1010). We estimated DNAm GA using two clocks specific to the Illumina Methylation EPIC 850K, the Haftorn clock and one developed from the Effects of Aspirin in Gestation and Reproduction study, in which women were followed to detect pregnancy at the earliest time possible. Among singletons, each week increase in DNAm GA was protective for overall delay (odds ratio:0.74; 95% confidence interval:0.61-0.90) and delay in all domains except for problem-solving skills. Among twins, we observed similar point estimates but lower precision. Results were similar for clinical GA. GAA was largely not associated with developmental delays. In summary, either DNAm GA or clinical GA at birth, but not epigenetic maturity (i.e. GAA), was associated with decreased odds of developmental delay in early childhood. Our study does not support using DNAm GA or GAA as separate risk factors for future risk of developmental delay within the first 3 years of age.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study.

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

Proteomic Risk Score of Increased Respiratory Susceptibility: A Multi-Cohort Study.

RATIONALE: Accelerated decline in lung function is associated with incident COPD, hospitalizations and death. However, identifying this trajectory with longitudinal spirometry measurements is challenging in clinical practice. OBJECTIVE: To determine whether a proteomic risk score trained on accelerated decline in lung function can assess risk of future respiratory disease and mortality. METHODS: In CARDIA, a population-based cohort starting in young adulthood, longitudinal measurements of FEV1 percent predicted (up to six timepoints over 30 years) were used to identify accelerated and normal decline trajectories. Protein aptamers associated with an accelerated decline trajectory were identified with multivariable logistic regression followed by LASSO regression. The proteomic respiratory susceptibility score was derived based on these circulating proteins and applied to the UK Biobank and COPDGene studies to examine associations with future respiratory morbidity and mortality. MEASUREMENTS AND RESULTS: Higher susceptibility score was independently associated with all-cause mortality (UKBB: HR 1.56, 95%CI 1.50-1.61; COPDGene: HR 1.75, 95%CI 1.63-1.88), respiratory mortality (UKBB: HR 2.39, 95% CI 2.16-2.64; COPDGene: HR 1.83, 95%CI 1.33-2.51), incident COPD (UKBB: HR 1.84, 95%CI 1.71-1.98), incident respiratory exacerbation (COPDGene: OR 1.11, 95%CI 1.03-1.20), and incident exacerbation requiring hospitalization (COPDGene: OR 1.18, 95%CI 1.08-1.28). CONCLUSIONS: A proteomic signature of increased respiratory susceptibility identifies people at risk of respiratory death, incident COPD, and respiratory exacerbations. This susceptibility score is comprised of proteins with well-known and novel associations with lung health and holds promise for the early detection of lung disease without requiring years of spirometry measurements.

Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts.

BACKGROUND: Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in white individuals, and they used proteomic measures from only one-time point. In this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black). MEDTHODS AND FINDINGS: A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 standard deviation were 1.65 and 1.38 (both p < 0.001) for all-cause mortality, 1.37 and 1.20 (both p < 0.001) for CVD mortality, 1.21 (p = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.46 and 1.68 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals. CONCLUSIONS: In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.

Proteomic profiling identifies novel proteins for genetic risk of severe COVID-19: the Atherosclerosis Risk in Communities Study.

BACKGROUND: Genome-wide association studies have identified six genetic variants associated with severe COVID-19, yet the mechanisms through which they may affect disease remains unclear. We investigated proteomic signatures related to COVID-19 risk variants rs657152 (ABO), rs10735079 (OAS1/OAS2/OAS3), rs2109069 (DPP9), rs74956615 (TYK2), rs2236757 (IFNAR2) and rs11385942 (SLC6A20/LZTFL1/CCR9/FYCO1/CXCR6/XCR1) as well as their corresponding downstream pathways that may promote severe COVID-19 in risk allele carriers and their potential relevancies to other infection outcomes. METHODS: A DNA aptamer-based array measured 4870 plasma proteins among 11 471 participants. Linear regression estimated associations between the COVID-19 risk variants and proteins with correction for multiple comparisons, and canonical pathway analysis was conducted. Cox regression assessed associations between proteins identified in the main analysis and risk of incident hospitalized respiratory infections (2570 events) over a 20.7-year follow-up. RESULTS: The ABO variant rs657152 was associated with 84 proteins in 7241 white participants with 24 replicated in 1671 Black participants. The TYK2 variant rs74956615 was associated with ICAM-1 and -5 in white participants with ICAM-5 replicated in Black participants. Of the 84 proteins identified in the main analysis, seven were significantly associated with incident hospitalized respiratory infections including Ephrin type-A receptor 4 (hazard ratio (HR): 0.87; P = 2.3 × 10-11) and von Willebrand factor type A (HR: 1.17; P = 1.6x10-13). CONCLUSIONS: Novel proteomics signatures and pathways for COVID-19-related risk variants TYK2 and ABO were identified. A subset of these proteins predicted greater risk of incident hospitalized pneumonia and respiratory infections. Further studies to examine these proteins in COVID-19 patients are warranted.

Extreme phenotype sampling and next generation sequencing to identify genetic variants associated with tacrolimus in African American kidney transplant recipients.

African American (AA) kidney transplant recipients (KTRs) have poor outcomes, which may in-part be due to tacrolimus (TAC) sub-optimal immunosuppression. We previously determined the common genetic regulators of TAC pharmacokinetics in AAs which were CYP3A5 *3, *6, and *7. To identify low-frequency variants that impact TAC pharmacokinetics, we used extreme phenotype sampling and compared individuals with extreme high (n = 58) and low (n = 60) TAC troughs (N = 515 AA KTRs). Targeted next generation sequencing was conducted in these two groups. Median TAC troughs in the high group were 7.7 ng/ml compared with 6.3 ng/ml in the low group, despite lower daily doses of 5 versus 12 mg, respectively. Of 34,542 identified variants across 99 genes, 1406 variants were suggestively associated with TAC troughs in univariate models (p-value < 0.05), however none were significant after multiple testing correction. We suggest future studies investigate additional sources of TAC pharmacokinetic variability such as drug-drug-gene interactions and pharmacomicrobiome.

Omega-3 Polyunsaturated Fatty Acids are not associated with Peripheral Artery Disease in a Meta-Analysis from the Multi-Ethnic Study of Atherosclerosis and Atherosclerosis Risk in Communities Study Cohorts.

BACKGROUND: Research suggests omega-3 polyunsaturated fatty acids (PUFAs) exert favorable effects on several biological processes involved in the development and progression of atherosclerotic cardiovascular disease (ASCVD). However, studies examining the relationship between omega-3 PUFAs and peripheral artery disease (PAD) are scarce. OBJECTIVES: We evaluated the associations between omega-3 PUFAs and incident PAD in a meta-analysis of the Multi-Ethnic Study of Atherosclerosis (MESA) and Atherosclerosis Risk in Communities (ARIC) study cohorts. METHODS: Omega-3 PUFAs eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were measured at baseline for all MESA (n = 6495) and Minnesota ARIC participants (n = 3612). Incident clinical PAD events (MESA n = 106; ARIC n = 149) identified primarily through ICD discharge codes were assessed through follow-up of each cohort. Associations between omega-3 PUFAs (EPA, DHA, and EPA+DHA) and incident PAD were modeled in MESA and ARIC as quartiles and continuously using Cox proportional hazards regression, respectively. A fixed-effects meta-analysis was conducted to evaluate associations in the 2 cohorts combined. RESULTS: In the fully adjusted model, in 10,107 participants, no significant associations were observed between EPA, DHA, or EPA+DHA, and incident PAD modeled as quartiles or continuously for either MESA or ARIC cohorts separately or in the meta-analysis after a follow-up of approximately 15 y. CONCLUSION: This study is consistent with previous literature indicating that the beneficial effects of omega-3 PUFAs on the markers of ASCVD may not translate to a clinically meaningful decrease in PAD risk.

Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence.

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.

Steroid-tacrolimus drug-drug interaction and the effect of CYP3A genotypes.

AIMS: Tacrolimus, metabolized by CYP3A4 and CYP3A5 enzymes, is susceptible to drug-drug interactions (DDI). Steroids induce CYP3A genes to increase tacrolimus clearance, but the effect is variable. We hypothesized that the extent of the steroid-tacrolimus DDI differs by CYP3A4/5 genotypes. METHODS: Kidney transplant recipients (n = 2462) were classified by the number of loss of function alleles (LOF) (CYP3A5*3, *6 and *7 and CYP3A4*22) and steroid use at each tacrolimus trough in the first 6 months post-transplant. A population pharmacokinetic analysis was performed by nonlinear mixed-effect modelling (NONMEM) and stepwise covariate modelling to define significant covariates affecting tacrolimus clearance. A stochastic simulation was performed and translated into a Shiny application with the mrgsolve and Shiny packages in R. RESULTS: Steroids were associated with modestly higher (3%-11.8%) tacrolimus clearance. Patients with 0-LOF alleles receiving steroids showed the greatest increase (11.8%) in clearance compared to no steroids, whereas those with 2-LOFs had a negligible increase (2.6%) in the presence of steroids. Steroid use increased tacrolimus clearance by 5% and 10.3% in patients with 1-LOF and 3/4-LOFs, respectively. CONCLUSIONS: Steroids increase the clearance of tacrolimus but vary slightly by CYP3A genotype. This is important in individuals of African ancestry who are more likely to carry no LOF alleles, may more commonly receive steroid treatment, and will need higher tacrolimus doses.

Proteomics Analysis of Genetic Liability of Abdominal Aortic Aneurysm Identifies Plasma Neogenin and Kit Ligand: The ARIC Study.

BACKGROUND: Genome-wide association studies have reported 23 gene loci related to abdominal aortic aneurysm (AAA)-a potentially lethal condition characterized by a weakened dilated vessel wall. This study aimed to identify proteomic signatures and pathways related to these risk loci to better characterize AAA genetic susceptibility. METHODS: Plasma concentrations of 4870 proteins were determined using a DNA aptamer-based array. Linear regression analysis estimated the associations between the 23 risk alleles and plasma protein levels with adjustments for potential confounders in a race-stratified analysis of 1671 Black and 7241 White participants. Significant proteins were then evaluated for their prediction of clinical AAA (454 AAA events in 11 064 individuals), and those significantly associated with AAA were further interrogated using Mendelian randomization analysis. RESULTS: Risk variants proximal to PSRC1-CELSR2-SORT1, PCIF1-ZNF335-MMP9, RP11-136O12.2/TRIB1, ZNF259/APOA5, IL6R, PCSK9, LPA, and APOE were associated with 118 plasma proteins in Whites and 59 were replicated in Black participants. Novel associations with clinical AAA incidence were observed for kit ligand (HR, 0.59 [95% CI, 0.42-0.82] for top versus first quintiles) and neogenin (HR, 0.64 [95% CI, 0.46-0.88]) over a median 21.2-year follow-up; neogenin was also associated with ultrasound-detected asymptomatic AAA (N=4295; 57 asymptomatic AAA cases). Mendelian randomization inverse variance weighted estimates suggested that AAA risk is promoted by lower levels of kit ligand (OR per SD=0.67; P=1.4×10-5) and neogenin (OR per SD=0.50; P=0.03). CONCLUSIONS: Low levels of neogenin and kit ligand may be novel risk factors for AAA development in potentially causal pathways. These findings provide insights and potential targets to reduce AAA susceptibility.

Limited Sampling Strategies Fail to Accurately Predict Mycophenolic Acid Area Under the Curve in Kidney Transplant Recipients and the Impact of Enterohepatic Recirculation.

BACKGROUND: Therapeutic drug monitoring for mycophenolic acid (MPA) is challenging due to difficulties in measuring the area under the curve (AUC). Limited sampling strategies (LSSs) have been developed for MPA therapeutic drug monitoring but come with risk of unacceptable performance. The authors hypothesized that the poor predictive performance of LSSs were due to the variability in MPA enterohepatic recirculation (EHR). This study is the first to evaluate LSSs models performance in the context of EHR. METHODS: Adult kidney transplant recipients (n = 84) receiving oral mycophenolate mofetil underwent intensive MPA pharmacokinetic sampling. MPA AUC0-12hr and EHR were determined. Published MPA LSSs in kidney transplant recipients receiving tacrolimus were evaluated for their predictive performance in estimating AUC0-12hr in our full cohort and separately in individuals with high and low EHR. RESULTS: None of the evaluated LSS models (n = 12) showed good precision or accuracy in predicting MPA AUC0-12hr in the full cohort. In the high EHR group, models with late timepoints had better accuracy but low precision, except for 1 model with late timepoints at 6 and 10 hours postdose, which had marginally acceptable precision. For all models, the good guess of predicted AUC0-12hr (±15% of observed AUC0-12hr) was highly variable (range, full cohort = 19%-61.9%; high EHR = 4.5%-65.9%; low EHR = 27.5%-62.5%). CONCLUSIONS: The predictive performance of the LSS models varied according to EHR status. Timepoints ≥5 hours postdose in LSS models are essential to capture EHR. Models and strategies that incorporate EHR during development are required to accurately ascertain MPA exposure.

Pulmonary Arterial Hypertension Patients Have a Proinflammatory Gut Microbiome and Altered Circulating Microbial Metabolites.

Rationale: Inflammation drives pulmonary arterial hypertension (PAH). Gut dysbiosis causes immune dysregulation and systemic inflammation by altering circulating microbial metabolites; however, little is known about gut dysbiosis and microbial metabolites in PAH. Objectives: To characterize the gut microbiome and microbial metabolites in patients with PAH. Methods: We performed 16S ribosomal RNA gene and shotgun metagenomics sequencing on stool from patients with PAH, family control subjects, and healthy control subjects. We measured markers of inflammation, gut permeability, and microbial metabolites in plasma from patients with PAH, family control subjects, and healthy control subjects. Measurements and Main Results: The gut microbiome was less diverse in patients with PAH. Shannon diversity index correlated with measures of pulmonary vascular disease but not with right ventricular function. Patients with PAH had a distinct gut microbial signature at the phylogenetic level, with fewer copies of gut microbial genes that produce antiinflammatory short-chain fatty acids (SCFAs) and secondary bile acids and lower relative abundances of species encoding these genes. Consistent with the gut microbial changes, patients with PAH had relatively lower plasma concentrations of SCFAs and secondary bile acids. Patients with PAH also had enrichment of species with the microbial genes that encoded the proinflammatory microbial metabolite trimethylamine. The changes in the gut microbiome and circulating microbial metabolites between patients with PAH and family control subjects were not as substantial as the differences between patients with PAH and healthy control subjects. Conclusions: Patients with PAH have proinflammatory gut dysbiosis, in which lower circulating SCFAs and secondary bile acids may facilitate pulmonary vascular disease. These findings support investigating modulation of the gut microbiome as a potential treatment for PAH.

Atherosclerotic Calcifications Have a Local Effect on the Peel Behavior of Human Aortic Media.

Aortic dissections, characterized by the propagation of a tear through the layers of the vessel wall, are critical, life-threatening events. Aortic calcifications are a common comorbidity in both acute and chronic dissections, yet their impact on dissection mechanics remains unclear. Using micro-computed tomography (CT) imaging, peel testing, and finite element modeling, this study examines the interplay between atherosclerotic calcifications and dissection mechanics. Samples cut from cadaveric human thoracic aortas were micro-CT imaged and subsequently peel-tested to map peel tension curves to the location of aortic calcifications. Empirical mode decomposition separated peel tension curves into high and low-frequency components, with high-frequency effects corresponding to interlamellar bonding mechanics and low-frequency effects to peel tension fluctuations. Finally, we used an idealized finite element model to examine how stiff calcifications affect aortic failure mechanics. Results showed that atherosclerosis influences dissection behavior on multiple length scales. Experimentally, atherosclerotic samples exhibited higher peel tensions and greater variance in the axial direction. The variation was driven by increased amplitudes of low-frequency tension fluctuations in diseased samples, indicating that more catastrophic propagations occur near calcifications. The simulations corroborated this finding, suggesting that the low-frequency changes resulted from the presence of a stiff calcification in the vessel wall. There were also modifications to the high-frequency peel mechanics, a response likely attributable to alterations in the microstructure and interlamellar bonding within the media. Considered collectively, these findings demonstrate that dissection mechanics are modified in aortic media nearby and adjacent to aortic calcifications.

Association between Circulating T Cells and the Gut Microbiome in Healthy Individuals: Findings from a Pilot Study.

Though the microbiome's impact on immune system homeostasis is well documented, the effect of circulating T cells on the gut microbiome remains unexamined. We analyzed data from 50 healthy volunteers in a pilot trial of aspirin, using immunophenotyping and 16S rRNA sequencing to evaluate the effect of baseline T cells on microbiome changes over 6 weeks. We employed an unsupervised sparse canonical correlation analysis (sCCA) and used multivariable linear regression models to evaluate the association between selected T cell subsets and selected bacterial genera after adjusting for covariates. In the cross-sectional analysis, percentages of naïve CD4+ T cells were positively associated with a relative abundance of Intestinimonas, and the percentage of activated CD8+ T cells was inversely associated with Cellulosibacter. In the longitudinal analysis, the baseline percentages of naïve CD4+ T cells and activated CD4+ T cells were inversely associated with a 6-week change in the relative abundance of Clostridium_XlVb and Anaerovorax, respectively. The baseline percentage of terminal effector CD4+ T cells was positively associated with the change in Flavonifractor. Notably, the microbiome taxa associated with T cell subsets exclusively belonged to the Bacillota phylum. These findings can guide future experimental studies focusing on the role of T cells in impacting gut microbiome homeostasis.

Proteomic analysis of diabetes genetic risk scores identifies complement C2 and neuropilin-2 as predictors of type 2 diabetes: the Atherosclerosis Risk in Communities (ARIC) Study.

AIMS/HYPOTHESIS: Genetic predisposition to type 2 diabetes is well-established, and genetic risk scores (GRS) have been developed that capture heritable liabilities for type 2 diabetes phenotypes. However, the proteins through which these genetic variants influence risk have not been thoroughly investigated. This study aimed to identify proteins and pathways through which type 2 diabetes risk variants may influence pathophysiology. METHODS: Using a proteomics data-driven approach in a discovery sample of 7241 White participants in the Atherosclerosis Risk in Communities Study (ARIC) cohort and a replication sample of 1674 Black ARIC participants, we interrogated plasma levels of 4870 proteins and four GRS of specific type 2 diabetes phenotypes related to beta cell function, insulin resistance, lipodystrophy, BMI/blood lipid abnormalities and a composite score of all variants combined. RESULTS: Twenty-two plasma proteins were identified in White participants after Bonferroni correction. Of the 22 protein-GRS associations that were statistically significant, 10 were replicated in Black participants and all but one were directionally consistent. In a secondary analysis, 18 of the 22 proteins were found to be associated with prevalent type 2 diabetes and ten proteins were associated with incident type 2 diabetes. Two-sample Mendelian randomisation indicated that complement C2 may be causally related to greater type 2 diabetes risk (inverse variance weighted estimate: OR 1.65 per SD; p=7.0 × 10-3), while neuropilin-2 was inversely associated (OR 0.44 per SD; p=8.0 × 10-3). CONCLUSIONS/INTERPRETATION: Identified proteins may represent viable intervention or pharmacological targets to prevent, reverse or slow type 2 diabetes progression, and further research is needed to pursue these targets.

Digital CRISPR Systems for the Next Generation of Nucleic Acid Quantification.

Digital CRISPR (dCRISPR) assays are an emerging platform of molecular diagnostics. Digital platforms introduce absolute quantification and increased sensitivity to bulk CRISPR assays. With ultra-specific targeting, isothermal operation, and rapid detection, dCRISPR systems are well-prepared to lead the field of molecular diagnostics. Here we summarized the common Cas proteins used in CRISPR detection assays. The methods of digital detection and critical performance factors are examined. We formed three strategies to frame the landscape of dCRISPR systems: (1) amplification free, (2) in-partition amplification, and (3) two-stage amplification. We also compared the performance of all systems through the limit of detection (LOD), testing time, and figure of merit (FOM). This work summarizes the details of digital CRISPR platforms to guide future development. We envision that improvements to LOD and dynamic range will position dCRISPR as the leading platform for the next generation of molecular biosensing.

Higher number of tacrolimus dose adjustments in kidney transplant recipients who are extensive and intermediate CYP3A5 metabolizers.

Kidney transplant recipients carrying the CYP3A5*1 allele have lower tacrolimus troughs, and higher dose requirements compared to those with the CYP3A5*3/*3 genotype. However, data on the effect of CYP3A5 alleles on post-transplant tacrolimus management are lacking. The effect of CYP3A5 metabolism phenotypes on the number of tacrolimus dose adjustments and troughs in the first 6 months post-transplant was evaluated in 78 recipients (64% Caucasians). Time to first therapeutic concentration, percentage of time in therapeutic range (TTR), and estimated glomerular filtration rate (eGFR) were also evaluated. Fifty-five kidney transplant recipients were CYP3A5 poor metabolizers (PM), 17 were intermediate metabolizers (IM), and 6 were extensive metabolizers (EM). Compared to PMs, EMs/IMs had significantly more dose adjustments (6.1 vs. 8.1, p = .015). Overall, 33.82% of trough measurements resulted in a dose change. There was no difference in the number of tacrolimus trough measurements between PMs and EM/IMs. The total daily tacrolimus dose requirements were higher in EMs and IMs compared to PMs (<.001). TTR was ∼50% in the PMs and EMs/IMs groups. CYP3A5 EM/IM metabolizers have more tacrolimus dose changes and higher dose requirements which increases clinical management complexity. Larger studies are needed to assess the cost and benefits of including genotyping data to improve clinical management.

Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia.

OBJECTIVE: Alzheimer's disease (AD) and AD related dementias (ADRD) are complex multifactorial neurodegenerative diseases. The associations between genetic variants obtained from genome wide association studies (GWAS) are the most widely available and well documented variants associated with ADRD. Application of deep learning methods to analyze large scale GWAS data may be a powerful approach to elucidate the biological mechanisms in ADRD compared to penalized regression models that may lead to over-fitting. METHODS: We developed a deep learning frame work explainable variational autoencoder (E-VAE) classifier model using genotype (GWAS SNPs = 5474) data from 2714 study participants in the Health and Retirement Study (HRS) to classify ADRD. We validated the generalizability of this model among 234 participants in the Religious Orders Study and Memory and Aging Project (ROSMAP). Utilizing a linear decoder approach we have extracted the weights associated with latent features for biological interpretation. RESULTS: We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset. CONCLUSION: This is the first study showing the generalizability of a deep learning prediction model for dementia using genetic variants in an independent cohort. The latent features identified using E-VAE can help us understand the biology of AD/ ADRD and better characterize disease status.

OMEGA-3 FATTY ACIDS ARE ASSOCIATED WITH DECREASED PRESENCE AND SEVERITY OF DIABETIC RETINOPATHY: A Combined Analysis of MESA and GOLDR Cohorts.

PURPOSE: Inflammation is associated with diabetic retinopathy development and progression, and previous studies have demonstrated that omega-3 polyunsaturated fatty acids have anti-inflammatory properties. Therefore, the goal of this study was to determine if omega-3 polyunsaturated fatty acids, docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), are associated with decreased risk and severity of retinopathy in individuals with type 2 diabetes. METHODS: In a combined population of 1,356 individuals with type 2 diabetes from the Multi-Ethnic Study of Atherosclerosis and Genetics of Latino Diabetic Retinopathy cohorts, odds ratios using logistic regression were determined to assess the association between polyunsaturated fatty acids and retinopathy. RESULTS: In 1,356 participants with type 2 diabetes, individuals in the fourth quartile of DHA were 17% less likely to have retinopathy compared with the first quartile ( P = 0.009, CI: 0.72-0.95). Secondary analysis revealed 38% lower severity of retinopathy in individuals in the fourth quartile compared with the first quartile of DHA ( P = 0.006; CI: 0.44-0.87) and EPA + DHA ( P = 0.004; CI: 0.44-0.85). No significant associations were observed between EPA and retinopathy. CONCLUSION: DHA is inversely associated with the presence and severity of diabetic retinopathy. Increased intake of dietary sources of DHA may provide some protection against retinopathy in individuals with type 2 diabetes and warrants more research as a preventative option.