About

Peter J. Snyder, M.D., is a Professor of Medicine in the Division of Endocrinology, Diabetes and Metabolism at the University of Pennsylvania's Perelman School of Medicine. He is also the Medical Director of the Penn Pituitary Center. His clinical expertise includes the diagnosis and treatment of pituitary adenomas and other pituitary and hypothalamic abnormalities, such as acromegaly, Cushing’s disease, hyperprolactinemia, gonadotroph and other nonfunctioning adenomas, hypopituitarism, and diabetes insipidus. Additionally, Dr. Snyder is interested in male reproductive endocrinology, focusing on hypogonadism and infertility in men. His research encompasses several areas, including the effects of hormones on bone structure, where he collaborates to study the impact of testosterone, growth hormone, estradiol, and other agents on bone microarchitecture and strength using magnetic resonance microimaging and finite element analysis. He is also involved in multicenter clinical trials such as The Testosterone Trial, which investigates the effects of testosterone in elderly men with low testosterone levels on various health outcomes. Dr. Snyder's work extends to understanding the clinical and biochemical characteristics of gonadotroph adenomas and evaluating the effects of somatostatin analogs like pasireotide on tumor size and secretion.

Research topics

• Medicine
• Internal medicine
• Psychology
• Biology
• Machine Learning
• Pathology
• Oncology
• Artificial Intelligence
• Computer Science
• Neuroscience
• Mathematics
• Theoretical computer science
• Psychiatry
• Econometrics
• Gastroenterology
• Library science
• Data science
• Genetics
• Gerontology

Selected publications

• Estimation of reference curves for brain atrophy and analysis of robustness to machine effects

  Scientific Reports · 2025-10-03 · 1 citations

  articleOpen access

  Neurodegenerative diseases like Alzheimer's are difficult to diagnose due to brain complexity and imaging variability. However, volumetric analysis tools, using reference curves, help detect abnormal brain atrophy and support diagnosis and monitoring. This study evaluates the robustness of three segmentation algorithms, AssemblyNet, FastSurfer and FreeSurfer, in constructing brain volume reference curves and detecting hippocampal atrophy. Using data from 3,730 cognitively normal subjects, we built reference curves and assessed robustness to magnetic field strength (1.5T vs. 3T) using four error metrics (sMAPE, sMSPE, wMAPE, sMdAPE) with bootstrap validation. We evaluated classification performance using hippocampal atrophy rates and HAVAs scores (Hippocampal-Amygdalo-Ventricular Atrophy scores). AssemblyNet shows the lowest errors across all robustness metrics. In contrast, FastSurfer and FreeSurfer exhibit greater deviations, indicating higher sensitivity to field strength variability. AssemblyNet provides consistent hippocampal atrophy rates across all reference models, despite slightly lower sensitivity, while FastSurfer and FreeSurfer display greater variability. Specificity ranges from 0.87 to 0.91 for AssemblyNet, compared to 0.76-0.93 for FastSurfer and 0.86-0.93 for FreeSurfer. Using the HAVAs score, all methods detect high atrophy rates in Alzheimer's patients. FastSurfer achieves the highest sensitivity (0.98), while AssemblyNet reaches the best specificity (0.95) and the highest balanced accuracy (0.91). This study underscores the importance of algorithm choice for reliable brain volumetric analysis in heterogeneous imaging environments. Among the methods tested, AssemblyNet stands out as both sensitive to Alzheimer's-related atrophy and robust to acquisition variability, making it a strong candidate when analyzing hippocampal volumes in large, multi-site datasets.

  PublisherOA PDFDOI

• Association and multimodal model of retinal and blood-based biomarkers for detection of preclinical Alzheimer’s disease

  Alzheimer s Research & Therapy · 2025-01-10 · 19 citations

  articleOpen access

  BACKGROUND: The potential diagnostic value of plasma amyloidogenic beta residue 42/40 ratio (Aβ42/Aβ40 ratio), neurofilament light (NfL), tau phosphorylated at threonine-181 (p-tau181), and threonine-217 (p-tau217) has been extensively discussed in the literature. We have also previously described the association between retinal biomarkers and preclinical Alzheimer's disease (AD). The goal of this study was to evaluate the association, and a multimodal model of, retinal and plasma biomarkers for detection of preclinical AD. METHODS: We included 82 cognitively unimpaired (CU) participants (141 eyes; mean age: 67 years; range: 56-80) from the Atlas of Retinal Imaging in Alzheimer's Study (ARIAS). Blood samples were assessed for concentrations of Aβ42/Aβ40 ratio, NfL, p-tau181, and p-tau217 (ALZpath, Inc.) using Single molecule array (SIMOA) technology. The Spectralis II system (Heidelberg Engineering) was used to acquire macular centered Spectral Domain Optical Coherence Tomography (SD-OCT) images for evaluation of putative retinal gliosis surface area and macular retinal nerve fiber layer (mRNFL) thickness. For all participants, correlations (adjusted for age and correlation between eyes) were assessed between retinal and blood-based biomarkers. A subgroup cohort of 57 eyes from 32 participants with recent Aβ positron emission tomography (PET) results, comprising 18 preclinical patients (Aβ PET + ve, 32 eyes) and 14 controls (Aβ PET -ve, 25 eyes) with a mean age of 69 vs. 66, p = 0.06, was included for the assessment of a multimodal model to distinguish between the two groups. For this subgroup cohort, receiver operating characteristic (ROC) analysis was performed to compare the multimodal model of retinal and plasma biomarkers vs. each biomarker alone to distinguish between the two groups. RESULTS: Significant correlation was found between putative retinal gliosis and p-tau217 in the univariate mixed model (β = 0.48, p = 0.007) but not for the other plasma biomarkers (p > 0.05). This positive correlation was also retained in the multivariate mixed model (β = 0.43, p = 0.022). The multimodal ROC model based on retinal (gliosis area, inner inferior RNFL thickness, inner superior RNFL thickness, and inner nasal RNFL thickness) and plasma biomarkers (p-tau217 and Aβ42/Aβ40 ratio) had an excellent AUC of 0.97 (95% CI = 0.93-1.01; p < 0.001) compared to unimodal models of retinal and plasma biomarkers. CONCLUSIONS: Our analyses show the potential of integrating retinal and blood-based biomarkers for improved detection and screening of preclinical AD.

  PublisherOA PDFDOI

• Surface-based morphometry reveals divergent aging trajectories in veterans with and without traumatic brain injury

  GeroScience · 2025-09-29

  articleOpen access

  Abstract This study investigates how traumatic brain injury (TBI) alters cortical aging by comparing cortical thickness (CT) and surface area (SA) in 34 brain regions between TBI survivors and age-matched controls. Using a cross-sectional retrospective design, 105 Vietnam Veterans (32 with moderate-to-severe TBI, 73 controls) were analyzed via surface-based morphometry. Principal Component Analysis (PCA) reduced dimensionality, and Multivariate Analysis of Covariance tested group differences while controlling for age, education, depression, Post-Traumatic Stress Disorder, and intracranial volume. Findings revealed divergent morphometric signatures of aging: the proportion of SA variance explained by the first principal component (PC1) was lower in the TBI cohort compared to controls, particularly in parietal and limbic regions. Conversely, CT variance explained by PC1 was higher in TBI compared to controls, with fewer factor loadings in frontal and occipital regions, suggesting differential structural aging patterns due to TBI. Regression analysis demonstrated a stronger association of SA with age in TBI (R 2 = 0.619, p = 0.01), while CT exhibited significant negative age-related thinning in TBI-specific regions (R 2 = 0.450, p &lt; 0.001). Together, these results suggest that TBI survivors exhibit structured, yet distinct, cortical remodeling, contrasting with the more diffuse patterns seen in normal aging. The differentiated organization of brain areas based on CT and SA points to brain morphology-based biomarkers capable of distinguishing pathological from normative aging trajectories. These biomarkers hold translational potential for refining diagnostic models of brain age and informing targeted neuromodulation or rehabilitation strategies to support cognitive and functional resilience in older adults with TBI. Graphical Abstract

  PublisherOA PDFDOI

• Clinical Practice Guideline on Cognitive Assessments for the Early Detection of Cognitive Impairment in Primary Care: A report from the Alzheimer's Association

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: Cognitive impairment often goes undetected by clinicians due to time constraints, limited expertise, and lack of standardized tools. Structured cognitive assessments improve detection accuracy compared to unstructured clinical judgment. In 2013, the Alzheimer's Association published recommendations to help primary care clinicians incorporate cognitive assessments into the Medicare Annual Wellness Visit. Since then, advances in cognitive assessment tools, availability of disease-modifying therapies, and shifting expectations around early diagnosis have highlighted the need for updated guidance. In response, the Alzheimer's Association is developing an evidence-based clinical practice guideline for cognitive testing in primary care, using the GRADE approach, informed by a systematic review of the best available evidence. METHOD: A multidisciplinary panel formulated a clinical question to inform the development of this guideline. A systematic review was conducted (search range: 1999-2024) to evaluate the diagnostic accuracy of ten cognitive tests (5-Cog, AD8, GPCOG, IQCODE, Mini-Cog, MIS, MoCA, QDRS, RUDAS, and SLUMS) for the early detection of cognitive impairment (including MCI and dementia), in adults aged 55+ in ambulatory settings. The panel also narratively summarized data on emerging digital tools. RESULT: Forty-one studies meeting eligibility criteria were included in the systematic review. Results were analyzed separately by test (including short-IQCODE and short-MoCA) and for primary care and for ambulatory clinic settings (e.g., neurology, memory disorder clinics), as well as for English and Spanish language, resulting in 21 separate analyses. Only three analyses, all related to MoCA, allowed for meta-analysis. The panel will use the evidence to inform conditional recommendations for or against each specific test. CONCLUSION: By evaluating the diagnostic performance of selected tests, this guideline aims to support healthcare professionals in primary care settings in effective triaging of individuals who may benefit from further cognitive evaluation. To enhance real-world applicability, considerations such as administration time, ease of scoring, and diagnostic accuracy - along with appropriate cut-offs by race/ethnicity, education, and language - should be prioritized. This guideline will be regularly updated over time to reflect emerging evidence and the needs of diverse populations.

  PublisherOA PDFDOI

• Cognition and modulation of the cholinergic system

  Handbook of clinical neurology · 2025-01-01 · 1 citations

  review1st authorCorresponding
  PublisherDOI

• Web Execution Bundles: Reproducible, Accurate, and Archivable Web Measurements

  ArXiv.org · 2025-01-27

  preprintOpen access

  Recently, reproducibility has become a cornerstone in the security and privacy research community, including artifact evaluations and even a new symposium topic. However, Web measurements lack tools that can be reused across many measurement tasks without modification, while being robust to circumvention, and accurate across the wide range of behaviors in the Web. As a result, most measurement studies use custom tools and varied archival formats, each of unknown correctness and significant limitations, systematically affecting the research's accuracy and reproducibility. To address these limitations, we present WebREC, a Web measurement tool that is, compared against the current state-of-the-art, accurate (i.e., correctly measures and attributes events not possible with existing tools), general (i.e., reusable without modification for a broad range of measurement tasks), and comprehensive (i.e., handling events from all relevant browser behaviors). We also present .web, an archival format for the accurate and reproducible measurement of a wide range of website behaviors. We empirically evaluate WebREC's accuracy by replicating well-known Web measurement studies and showing that WebREC's results more accurately match our baseline. We then assess if WebREC and .web succeed as general-purpose tools, which could be used to accomplish many Web measurement tasks without modification. We find that this is so: 70% of papers discussed in a 2024 web crawling SoK paper could be conducted using WebREC as is, and a larger number (48%) could be leveraged against .web archives without requiring any new crawling.

  PublisherOA PDFDOI

• Depressive Symptoms and Amyloid Pathology

  JAMA Psychiatry · 2025-01-22 · 19 citations

  articleOpen access

  Importance: Depressive symptoms are associated with cognitive decline in older individuals. Uncertainty about underlying mechanisms hampers diagnostic and therapeutic efforts. This large-scale study aimed to elucidate the association between depressive symptoms and amyloid pathology. Objective: To examine the association between depressive symptoms and amyloid pathology and its dependency on age, sex, education, and APOE genotype in older individuals without dementia. Design, Setting, and Participants: Cross-sectional analyses were performed using data from the Amyloid Biomarker Study data pooling initiative. Data from 49 research, population-based, and memory clinic studies were pooled and harmonized. The Amyloid Biomarker Study has been collecting data since 2012 and data collection is ongoing. At the time of analysis, 95 centers were included in the Amyloid Biomarker Study. The study included 9746 individuals with normal cognition (NC) and 3023 participants with mild cognitive impairment (MCI) aged between 34 and 100 years for whom data on amyloid biomarkers, presence of depressive symptoms, and age were available. Data were analyzed from December 2022 to February 2024. Main Outcomes and Measures: Amyloid-β1-42 levels in cerebrospinal fluid or amyloid positron emission tomography scans were used to determine presence or absence of amyloid pathology. Presence of depressive symptoms was determined on the basis of validated depression rating scale scores, evidence of a current clinical diagnosis of depression, or self-reported depressive symptoms. Results: In individuals with NC (mean [SD] age, 68.6 [8.9] years; 5664 [58.2%] female; 3002 [34.0%] APOE ε4 carriers; 937 [9.6%] had depressive symptoms; 2648 [27.2%] had amyloid pathology), the presence of depressive symptoms was not associated with amyloid pathology (odds ratio [OR], 1.13; 95% CI, 0.90-1.40; P = .29). In individuals with MCI (mean [SD] age, 70.2 [8.7] years; 1481 [49.0%] female; 1046 [44.8%] APOE ε4 carriers; 824 [27.3%] had depressive symptoms; 1668 [55.8%] had amyloid pathology), the presence of depressive symptoms was associated with a lower likelihood of amyloid pathology (OR, 0.73; 95% CI 0.61-0.89; P = .001). When considering subgroup effects, in individuals with NC, the presence of depressive symptoms was associated with a higher frequency of amyloid pathology in APOE ε4 noncarriers (mean difference, 5.0%; 95% CI 1.0-9.0; P = .02) but not in APOE ε4 carriers. This was not the case in individuals with MCI. Conclusions and Relevance: Depressive symptoms were not consistently associated with a higher frequency of amyloid pathology in participants with NC and were associated with a lower likelihood of amyloid pathology in participants with MCI. These findings were not influenced by age, sex, or education level. Mechanisms other than amyloid accumulation may commonly underlie depressive symptoms in late life.

  PublisherOA PDFDOI

• Retinal Layer Thickness and PET Centiloid Values in Cognitively Unimpaired Older Adults

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: Beta amyloid (Aβ) PET results are quantified in centiloids to standardize cerebral Aβ burden, an established Alzheimer's disease (AD) biomarker. Spectral domain optical coherence tomography (SD-OCT) imaging studies of the retina support retinal layer thinning as a biomarker of AD. Research examining the relationships between cerebral Aβ burden (in centiloids) and retinal layer thickness in preclinical AD remains understudied. This study aims to (1) examine the relationship between retinal layer thicknesses and PET centiloid values and (2) identify which retinal layers may be biomarkers of preclinical AD. METHOD: Heidelberg SPECTRALIS captured SD-OCT images from 40 cognitively unimpaired older adults (ages 65-80; mean=67.8). HEYEX software computed the ETDRS thickness maps for each retinal layer (mRNFL, GCL, IPL, INL, OPL, ONL, IRL, ORL, RPE). In this analysis, retinal structure measurements (thickness, volume) were averaged for each layer (i.e., full layer, central quadrant, inner ring, outer ring). PET scans using florbetaben were conducted within six weeks of retinal imaging. PET results were quantified via centiloid scale combined with visual reads to determine PET status: positive (n = 10) or negative (n = 30). Linear regressions, controlling for age, examined whether retinal thickness averages predicted centiloid values. Logistic regressions, controlling for age, examined whether retinal thicknesses predicted binary PET results. RESULT: Retinal mRNFL outer thickness significantly predicted PET centiloid value (p = 0.0206). Trends were seen in the relationship between mRNFL total layer thickness and PET centiloid (p = 0.0921) as well as mRNFL total layer volume and PET centiloid (p = 0.0679). The relationship between RPE outer thickness and PET status (p = 0.0757) and OPL total layer volume and PET status (p = 0.0572) was trending toward significance. CONCLUSION: The significant relationship between mRNFL and centiloid values aligned with previous work showing mRNFL thinning in MCI and AD. This is the first study to examine this relationship in a preclinical population. Retinal OPL findings replicate prior research by our group which demonstrated a relationship between OPL thickness and plasma ptau217, an indicator of cerebral amyloidosis. Further studies of longitudinal changes are needed to validate retinal biomarkers in preclinical AD.

  PublisherOA PDFDOI

• The relationship between gait task performance and AD plasma biomarkers in cognitively unimpaired older adults and patients with mild cognitive impairment

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: Gait impairments in Alzheimer's disease (AD) and related dementias pose a major fall risk/contribute to morbidity/mortality. The Timed Up and Go (TUG) test is often used to assess mobility, gait changes, and dual-task performance. The TUG-Dual Task (TUG-DT) version adds serial subtraction exercises to evaluate dual-task cost (DTC). For cognitively unimpaired (CU) individuals or those with mild cognitive impairment (MCI), plasma biomarkers like pTau217, pTau181, and neurofilament light chain (NfL) can help assess the risk of AD. This study aimed to explore the relationship between TUG performance and plasma biomarkers in CU and MCI patients. METHODS: Participants included CU low-risk (n = 75), CU high-risk (n = 87), and CI (n = 32) older adults aged 55-80, mean = .67.28 ± 6.062 years. Cognitive ability was assessed using the Clinical Dementia Rating Scale (CU = 0; CI = 0.5 or 1.0) and the Montreal Cognitive Assessment (CU ≥ 26; 18 ≤ CI ≤ 26). AD-risk was determined by APOE genotyping and family history for CU groups. Plasma biomarkers pTau217, pTau181, and NfL were analyzed from fasting blood draws. Participants completed the TUG and TUG-DT. ANOVAs, ANCOVAs, logistic regression, and generalized additive models (GAMs), were used to analyze the relationship between demographic factors, gait performance, and plasma biomarkers, with model comparisons guiding the final choice of GAMs for their flexibility in handling non-linear relationships. RESULTS: Step count analysis on the TUG showed that the CU-high-risk and MCI groups performed similarly, while the CU-low-risk group completed significantly fewer steps than both. Plasma biomarkers, particularly pTau181 and NfL, interacted to predict gait performance only in the CU high-risk group. CONCLUSIONS: The TUG can predict plasma pTau217 levels with high specificity, distinguishing CU individuals not at risk for AD. Additionally, pTau181 and NfL interacted to predict performance on the TUG and TUG-DT in the CU-high-risk group, suggesting subtle gait changes may signal early AD pathology. The CU-low-risk group's reduced step count compared to others indicates preclinical AD might manifest with subtle mobility impairments. These findings support using simple gait tasks like the TUG for AD risk assessment in older adults.

  PublisherOA PDFDOI

• Dynamic proportional loss of functional connectivity revealed change of left superior frontal gyrus in subjective cognitive decline: an explanatory study based on Chinese and Western cohorts

  GeroScience · 2025-01-31 · 2 citations

  articleOpen access
  PublisherOA PDFDOI

Recent grants

• NIH Grant R01HD013869

  NIH · $166k · 1987

• NIH Grant R01DK042139

  NIH · $1.2M · 2000

• NIH Grant M01RR000040

  NIH · $7.2M · 2007

Frequent coauthors

• Paul Maruff

  Monash University

  309 shared

• Brian R. Ott

  Brown University

  280 shared

• Stephen Salloway

  257 shared

• Yen Ying Lim

  Monash University

  246 shared

• John C. Morris

  Washington University in St. Louis

  228 shared

• Adam Fleisher

  Eli Lilly (United States)

  206 shared

• Michael Donohue

  Janssen (United States)

  201 shared

• Clifford R. Jack

  WinnMed

  196 shared

Labs

• Peter J. Snyder LaboratoryPI

Education

• Clinical Neurosciences Post-Doctoral Fellowship, Neurology & Psychiatry

  North Shore Long Island Jewish Health System

  1994

• Neuropsychology Residency , Neurology & Psychiatry

  Albert Einstein College of Medicine, Yeshiva University

  1992

• Ph.D. (Clinical Neuropsychology), Psychology

  Michigan State University

  1992

• M.A. (Clinical Psychology / Neuroscience), Psychology

  Michigan State University

  1988

• A.B. with High Honors (Neuroscience), Psychology

  University of Michigan

  1986