Daniel Panyard

All Publications

• Plasma proteomics and carotid intima-media thickness in the UK biobank cohort. Frontiers in cardiovascular medicine Chen, M. L., Kho, P. F., Guarischi-Sousa, R., Zhou, J., Panyard, D. J., Azizi, Z., Gupte, T., Watson, K., Abbasi, F., Assimes, T. L. 2024; 11: 1478600

  Abstract

  Ultrasound derived carotid intima-media thickness (cIMT) is valuable for cardiovascular risk stratification. We assessed the relative importance of traditional atherosclerosis risk factors and plasma proteins in predicting cIMT measured nearly a decade later.We examined 6,136 UK Biobank participants with 1,461 proteins profiled using the proximity extension assay applied to their baseline blood draw who subsequently underwent a cIMT measurement. We implemented linear regression, stepwise Akaike Information Criterion-based, and the least absolute shrinkage and selection operator (LASSO) models to identify potential proteomic as well as non-proteomic predictors. We evaluated our model performance using the proportion variance explained (R 2).The mean time from baseline assessment to cIMT measurement was 9.2 years. Age, blood pressure, and anthropometric related variables were the strongest predictors of cIMT with fat-free mass index of the truncal region being the strongest predictor among adiposity measurements. A LASSO model incorporating variables including age, assessment center, genetic risk factors, smoking, blood pressure, trunk fat-free mass index, apolipoprotein B, and Townsend deprivation index combined with 97 proteins achieved the highest R 2 (0.308, 95% C.I. 0.274, 0.341). In contrast, models built with proteins alone or non-proteomic variables alone explained a notably lower R 2 (0.261, 0.228-0.294 and 0.260, 0.226-0.293, respectively). Chromogranin b (CHGB), Cystatin-M/E (CST6), leptin (LEP), and prolargin (PRELP) were the proteins consistently selected across all models.Plasma proteins add to the clinical and genetic risk factors in predicting a cIMT measurement. Our findings implicate blood pressure and extracellular matrix-related proteins in cIMT pathophysiology.

  View details for DOI 10.3389/fcvm.2024.1478600

  View details for PubMedID 39416432

  View details for PubMedCentralID PMC11480011

• A plasma proteomic signature for atherosclerotic cardiovascular disease risk prediction in the UK Biobank cohort. medRxiv : the preprint server for health sciences Gupte, T. P., Azizi, Z., Kho, P. F., Zhou, J., Chen, M., Panyard, D. J., Guarischi-Sousa, R., Hilliard, A. T., Sharma, D., Watson, K., Abbasi, F., Tsao, P. S., Clarke, S. L., Assimes, T. L. 2024

  Abstract

  Background: While risk stratification for atherosclerotic cardiovascular disease (ASCVD) is essential for primary prevention, current clinical risk algorithms demonstrate variability and leave room for further improvement. The plasma proteome holds promise as a future diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict ASCVD.Method: Clinical, genetic, and high-throughput plasma proteomic data were analyzed for association with ASCVD in a cohort of 41,650 UK Biobank participants. Selected features for analysis included clinical variables such as a UK-based cardiovascular clinical risk score (QRISK3) and lipid levels, 36 polygenic risk scores (PRSs), and Olink protein expression data of 2,920 proteins. We used least absolute shrinkage and selection operator (LASSO) regression to select features and compared area under the curve (AUC) statistics between data types. Randomized LASSO regression with a stability selection algorithm identified a smaller set of more robustly associated proteins. The benefit of plasma proteins over standard clinical variables, the QRISK3 score, and PRSs was evaluated through the derivation of Delta AUC values. We also assessed the incremental gain in model performance using proteomic datasets with varying numbers of proteins. To identify potential causal proteins for ASCVD, we conducted a two-sample Mendelian randomization (MR) analysis.Result: The mean age of our cohort was 56.0 years, 60.3% were female, and 9.8% developed incident ASCVD over a median follow-up of 6.9 years. A protein-only LASSO model selected 294 proteins and returned an AUC of 0.723 (95% CI 0.708-0.737). A clinical variable and PRS-only LASSO model selected 4 clinical variables and 20 PRSs and achieved an AUC of 0.726 (95% CI 0.712-0.741). The addition of the full proteomic dataset to clinical variables and PRSs resulted in a Delta AUC of 0.010 (95% CI 0.003-0.018). Fifteen proteins selected by a stability selection algorithm offered improvement in ASCVD prediction over the QRISK3 risk score [Delta AUC: 0.013 (95% CI 0.005-0.021)]. Filtered and clustered versions of the full proteomic dataset (consisting of 600-1,500 proteins) performed comparably to the full dataset for ASCVD prediction. Using MR, we identified 11 proteins as potentially causal for ASCVD.Conclusion: A plasma proteomic signature performs well for incident ASCVD prediction but only modestly improves prediction over clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of this signature in predicting the risk of ASCVD over the standard practice of using the QRISK3 score.

  View details for DOI 10.1101/2024.09.13.24313652

  View details for PubMedID 39314942

• Plasma proteomic signatures for type 2 diabetes mellitus and related traits in the UK Biobank cohort. medRxiv : the preprint server for health sciences Gupte, T. P., Azizi, Z., Kho, P. F., Zhou, J., Nzenkue, K., Chen, M., Panyard, D. J., Guarischi-Sousa, R., Hilliard, A. T., Sharma, D., Watson, K., Abbasi, F., Tsao, P. S., Clarke, S. L., Assimes, T. L. 2024

  Abstract

  Aims/hypothesis: The plasma proteome holds promise as a diagnostic and prognostic tool that can accurately reflect complex human traits and disease processes. We assessed the ability of plasma proteins to predict type 2 diabetes mellitus (T2DM) and related traits.Methods: Clinical, genetic, and high-throughput proteomic data from three subcohorts of UK Biobank participants were analyzed for association with dual-energy x-ray absorptiometry (DXA) derived truncal fat (in the adiposity subcohort), estimated maximum oxygen consumption (VO 2 max) (in the fitness subcohort), and incident T2DM (in the T2DM subcohort). We used least absolute shrinkage and selection operator (LASSO) regression to assess the relative ability of non-proteomic and proteomic variables to associate with each trait by comparing variance explained (R 2 ) and area under the curve (AUC) statistics between data types. Stability selection with randomized LASSO regression identified the most robustly associated proteins for each trait. The benefit of proteomic signatures (PSs) over QDiabetes, a T2DM clinical risk score, was evaluated through the derivation of delta (Delta) AUC values. We also assessed the incremental gain in model performance metrics using proteomic datasets with varying numbers of proteins. A series of two-sample Mendelian randomization (MR) analyses were conducted to identify potentially causal proteins for adiposity, fitness, and T2DM.Results: Across all three subcohorts, the mean age was 56.7 years and 54.9% were female. In the T2DM subcohort, 5.8% developed incident T2DM over a median follow-up of 7.6 years. LASSO-derived PSs increased the R 2 of truncal fat and VO 2 max over clinical and genetic factors by 0.074 and 0.057, respectively. We observed a similar improvement in T2DM prediction over the QDiabetes score [Delta AUC: 0.016 (95% CI 0.008, 0.024)] when using a robust PS derived strictly from the T2DM outcome versus a model further augmented with non-overlapping proteins associated with adiposity and fitness. A small number of proteins (29 for truncal adiposity, 18 for VO2max, and 26 for T2DM) identified by stability selection algorithms offered most of the improvement in prediction of each outcome. Filtered and clustered versions of the full proteomic dataset supplied by the UK Biobank (ranging between 600-1,500 proteins) performed comparably to the full dataset for T2DM prediction. Using MR, we identified 4 proteins as potentially causal for adiposity, 1 as potentially causal for fitness, and 4 as potentially causal for T2DM.Conclusions/Interpretation: Plasma PSs modestly improve the prediction of incident T2DM over that possible with clinical and genetic factors. Further studies are warranted to better elucidate the clinical utility of these signatures in predicting the risk of T2DM over the standard practice of using the QDiabetes score. Candidate causally associated proteins identified through MR deserve further study as potential novel therapeutic targets for T2DM.

  View details for DOI 10.1101/2024.09.13.24313501

  View details for PubMedID 39314935

• Associations between accurate measures of adiposity and fitness, blood proteins, and insulin sensitivity among South Asians and Europeans. medRxiv : the preprint server for health sciences Kho, P. F., Stell, L., Jimenez, S., Zanetti, D., Panyard, D. J., Watson, K. L., Sarraju, A., Chen, M. L., Lind, L., Petrie, J. R., Chan, K. N., Fonda, H., Kent, K., Myers, J. N., Palaniappan, L., Abbasi, F., Assimes, T. L. 2024

  Abstract

  South Asians (SAs) may possess a unique predisposition to insulin resistance (IR). We explored this possibility by investigating the relationship between 'gold standard' measures of adiposity, fitness, selected proteomic biomarkers, and insulin sensitivity among a cohort of SAs and Europeans (EURs).A total of 46 SAs and 41 EURs completed 'conventional' (lifestyle questionnaires, standard physical exam) as well as 'gold standard' (dual energy X-ray absorptiometry scan, cardiopulmonary exercise test, and insulin suppression test) assessments of adiposity, fitness, and insulin sensitivity. In a subset of 28 SAs and 36 EURs, we also measured the blood-levels of eleven IR-related proteins. We conducted Spearman correlation to identify correlates of steady-state plasma glucose (SSPG) derived from the insulin suppression test, followed by multivariable linear regression analyses of SSPG, adjusting for age, sex and ancestral group.Sixteen of 30 measures significantly associated with SSPG, including one conventional and eight gold standard measures of adiposity, one conventional and one gold standard measure of fitness, and five proteins. Multivariable regressions revealed that gold standard measures and plasma proteins attenuated ancestral group differences in IR, suggesting their potential utility in assessing IR, especially among SAs.Ancestral group differences in IR may be explained by accurate measures of adiposity and fitness, with specific proteins possibly serving as useful surrogates for these measures, particularly for SAs.

  View details for DOI 10.1101/2024.09.06.24313199

  View details for PubMedID 39281745

  View details for PubMedCentralID PMC11398600

• Post-GWAS multiomic functional investigation of the TNIP1 locus in Alzheimer's disease highlights a potential role for GPX3. Alzheimer's & dementia : the journal of the Alzheimer's Association Panyard, D. J., Reus, L. M., Ali, M., Liu, J., Deming, Y. K., Lu, Q., Kollmorgen, G., Carboni, M., Wild, N., Visser, P. J., Bertram, L., Zetterberg, H., Blennow, K., Gobom, J., Western, D., Sung, Y. J., Carlsson, C. M., Johnson, S. C., Asthana, S., Cruchaga, C., Tijms, B. M., Engelman, C. D., Snyder, M. P. 2024

  Abstract

  Recent genome-wide association studies (GWAS) have reported a genetic association with Alzheimer's disease (AD) at the TNIP1/GPX3 locus, but the mechanism is unclear.We used cerebrospinal fluid (CSF) proteomics data to test (n = 137) and replicate (n = 446) the association of glutathione peroxidase 3 (GPX3) with CSF biomarkers (including amyloid and tau) and the GWAS-implicated variants (rs34294852 and rs871269).CSF GPX3 levels decreased with amyloid and tau positivity (analysis of variance P = 1.5 × 10-5) and higher CSF phosphorylated tau (p-tau) levels (P = 9.28 × 10-7). The rs34294852 minor allele was associated with decreased GPX3 (P = 0.041). The replication cohort found associations of GPX3 with amyloid and tau positivity (P = 2.56 × 10-6) and CSF p-tau levels (P = 4.38 × 10-9).These results suggest variants in the TNIP1 locus may affect the oxidative stress response in AD via altered GPX3 levels.Cerebrospinal fluid (CSF) glutathione peroxidase 3 (GPX3) levels decreased with amyloid and tau positivity and higher CSF phosphorylated tau. The minor allele of rs34294852 was associated with lower CSF GPX3. levels when also controlling for amyloid and tau category. GPX3 transcript levels in the prefrontal cortex were lower in Alzheimer's disease than controls. rs34294852 is an expression quantitative trait locus for GPX3 in blood, neutrophils, and microglia.

  View details for DOI 10.1002/alz.13848

  View details for PubMedID 38809917

• PLASMA PROTEOMICS AND VISCERAL ADIPOSE TISSUE VOLUME: A MACHINE LEARNING ANALYSIS OF INTERACTION BETWEEN BIOMARKERS, SOCIO-BEHAVIORAL, AND FITNESS FACTORS IN UK BIOBANK Azizi, Z., Gupte, T., Kho, P., Nzenkue, K., Zhou, J., Guarischi-Sousa, R., Panyard, D., Chen, M., Abbasi, F., Clarke, S., Tsao, P., Assimes, T. L. ELSEVIER SCIENCE INC. 2024: 1699

  View details for Web of Science ID 001324901501731

• Assessing the Biological Mechanisms Linking Smoking Behavior and Cognitive Function: A Mediation Analysis of Untargeted Metabolomics. Metabolites Choi, J. J., Koscik, R. L., Jonaitis, E. M., Panyard, D. J., Morrow, A. R., Johnson, S. C., Engelman, C. D., Schmitz, L. L. 2023; 13 (11)

  Abstract

  (1) Smoking is the most significant preventable health hazard in the modern world. It increases the risk of vascular problems, which are also risk factors for dementia. In addition, toxins in cigarettes increase oxidative stress and inflammation, which have both been linked to the development of Alzheimer's disease and related dementias (ADRD). This study identified potential mechanisms of the smoking-cognitive function relationship using metabolomics data from the longitudinal Wisconsin Registry for Alzheimer's Prevention (WRAP). (2) 1266 WRAP participants were included to assess the association between smoking status and four cognitive composite scores. Next, untargeted metabolomic data were used to assess the relationships between smoking and metabolites. Metabolites significantly associated with smoking were then tested for association with cognitive composite scores. Total effect models and mediation models were used to explore the role of metabolites in smoking-cognitive function pathways. (3) Plasma N-acetylneuraminate was associated with smoking status Preclinical Alzheimer Cognitive Composite 3 (PACC3) and Immediate Learning (IMM). N-acetylneuraminate mediated 12% of the smoking-PACC3 relationship and 13% of the smoking-IMM relationship. (4) These findings provide links between previous studies that can enhance our understanding of potential biological pathways between smoking and cognitive function.

  View details for DOI 10.3390/metabo13111154

  View details for PubMedID 37999250

  View details for PubMedCentralID PMC10673384

• Unique genetic architecture of CSF and brain metabolites pinpoints the novel targets for the traits of human wellness. Research square Wang, C., Western, D., Yang, C., Ali, M., Wang, L., Gorijala, P., Timsina, J., Ruiz, A., Pastor, P., Fernandez, M., Panyard, D., Engelman, C., Deming, Y., Boada, M., Cano, A., García-González, P., Graff-Radford, N., Mori, H., Lee, J. H., Perrin, R., Sung, Y. J., Cruchaga, C. 2023

  Abstract

  Brain metabolism perturbation can contribute to traits and diseases. We conducted the first large-scale CSF and brain genome-wide association studies, which identified 219 independent associations (59.8% novel) for 144 CSF metabolites and 36 independent associations (55.6% novel) for 34 brain metabolites. Most of the novel signals (97.7% and 70.0% in CSF and brain) were tissue specific. We also integrated MWAS-FUSION approaches with Mendelian Randomization and colocalization to identify causal metabolites for 27 brain and human wellness phenotypes and identified eight metabolites to be causal for eight traits (11 relationships). Low mannose level was causal to bipolar disorder and as dietary supplement it may provide therapeutic benefits. Low galactosylglycerol level was found causal to Parkinson's Disease (PD). Our study expanded the knowledge of MQTL in central nervous system, provided insights into human wellness, and successfully demonstrates the utility of combined statistical approaches to inform interventions.

  View details for DOI 10.21203/rs.3.rs-2923409/v1

  View details for PubMedID 37333177

  View details for PubMedCentralID PMC10274943

• Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nature biomedical engineering Shen, X., Kellogg, R., Panyard, D. J., Bararpour, N., Castillo, K. E., Lee-McMullen, B., Delfarah, A., Ubellacker, J., Ahadi, S., Rosenberg-Hasson, Y., Ganz, A., Contrepois, K., Michael, B., Simms, I., Wang, C., Hornburg, D., Snyder, M. P. 2023

  Abstract

  Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

  View details for DOI 10.1038/s41551-022-00999-8

  View details for PubMedID 36658343

• Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis JOURNAL OF ALZHEIMERS DISEASE Panyard, D. J., Deming, Y. K., Darst, B. F., Van Hulle, C. A., Zetterberg, H., Blennow, K., Kollmorgen, G., Suridjan, I., Carlsson, C. M., Johnson, S. C., Asthana, S., Engelman, C. D., Lu, Q. 2023; 92 (2): 395-409

  Abstract

  Our understanding of the pathophysiology underlying Alzheimer's disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models.We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers.We built 13 tissue-specific AD PRS and studied the scores' relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD.The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67-2.78), p = 3.62×10-9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ42:Aβ40 ratio, p = 3.53×10-6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10-5).These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.

  View details for DOI 10.3233/JAD-220599

  View details for Web of Science ID 000952023800002

  View details for PubMedID 36744333

  View details for PubMedCentralID PMC10050104

• The metabolomics of human aging: Advances, challenges, and opportunities. Science advances Panyard, D. J., Yu, B., Snyder, M. P. 2022; 8 (42): eadd6155

  Abstract

  As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

  View details for DOI 10.1126/sciadv.add6155

  View details for PubMedID 36260671

• Cerebrospinal Fluid Sphingomyelins in Alzheimer's Disease, Neurodegeneration, and Neuroinflammation. Journal of Alzheimer's disease : JAD Morrow, A., Panyard, D. J., Deming, Y. K., Jonaitis, E., Dong, R., Vasiljevic, E., Betthauser, T. J., Kollmorgen, G., Suridjan, I., Bayfield, A., Van Hulle, C. A., Zetterberg, H., Blennow, K., Carlsson, C. M., Asthana, S., Johnson, S. C., Engelman, C. D. 2022

  Abstract

  BACKGROUND: Sphingomyelin (SM) levels have been associated with Alzheimer's disease (AD), but the association direction has been inconsistent and research on cerebrospinal fluid (CSF) SMs has been limited by sample size, breadth of SMs examined, and diversity of biomarkers available.OBJECTIVE: Here, we seek to build on our understanding of the role of SM metabolites in AD by studying a broad range of CSF SMs and biomarkers of AD, neurodegeneration, and neuroinflammation.METHODS: Leveraging two longitudinal AD cohorts with metabolome-wide CSF metabolomics data (n = 502), we analyzed the relationship between the levels of 12 CSF SMs, and AD diagnosis and biomarkers of pathology, neurodegeneration, and neuroinflammation using logistic, linear, and linear mixed effects models.RESULTS: No SMs were significantly associated with AD diagnosis, mild cognitive impairment, or amyloid biomarkers. Phosphorylated tau, neurofilament light, alpha-synuclein, neurogranin, soluble triggering receptor expressed on myeloid cells 2, and chitinase-3-like-protein 1 were each significantly, positively associated with at least 5 of the SMs.CONCLUSION: The associations between SMs and biomarkers of neurodegeneration and neuroinflammation, but not biomarkers of amyloid or diagnosis of AD, point to SMs as potential biomarkers for neurodegeneration and neuroinflammation that may not be AD-specific.

  View details for DOI 10.3233/JAD-220349

  View details for PubMedID 36155504