Fan, Winnie Z.

Abstract INTRODUCTION We describe the reliability of remote self‐administered digital cognitive measures completed via the Mayo Test Drive (MTD) web‐based platform. METHODS A total of 1846 participants (mean age = 70, SD = 12, range 31–101; 48% male; 96% White; 99% non‐Hispanic; 97% cognitively unimpaired) with two to four complete MTD sessions at ∼7.5‐month intervals were included. Test‐retest reliability was assessed using single‐rating, absolute‐agreement, and two‐way mixed intraclass correlation coefficients (ICCs) with 95% confidence intervals. ICCs for in‐person‐administered traditional neuropsychological measures were compared to MTD for a subset of 244 participants. Analyses also compared reliability across demographic, residential, and session context subgroups. RESULTS Reliability was good for the MTD Composite (total ICC = 0.79 [0.77, 0.80]), and moderate‐to‐good for the primary outcome variables for each MTD subtest (total ICCs 0.70–0.83 for Stricker Learning Span and Symbols). The reliability of the remote self‐administered MTD was similar to in‐person‐administered cognitive measures. DISCUSSION MTD showed moderate‐to‐good reliability, supporting its use in longitudinal monitoring.

Abstract Background Self‐administered and remote digital cognitive assessments provide a scalable method for screening and longitudinal cognitive monitoring. Mayo Test Drive (MTD) is one such assessment with previously demonstrated usability, reliability, validity and multi‐device compatibility. MTD includes a computer‐adaptive word list memory test (Stricker Learning Span) and a measure of processing speed/executive functioning (Symbols Test) that combine to provide an MTD Composite. This study aimed to develop a conditional normative model predicting longitudinal cognitive change for MTD, and to validate the model in two independent samples of cognitively unimpaired (CU) and mild cognitive impairment (MCI)/dementia (DEM) participants. Method 1355 CU participants at initial MTD assessment with 2‐4 consecutive MTD assessments every 7.5 months comprised the development dataset. Linear mixed effects models were used to construct a conditional normative model for MTD assessments 2‐4. The baseline model was chosen a priori , consisting of age, sex, education, baseline score, number of assessments, and time. Device type, race, and baseline score by time interactions were also considered. Variables were only added if a >1% increment in r‐squared was observed. The models were externally validated in 113 CU and 51 MCI/DEM patients. Following model development, the data were updated and additional follow‐up MTD assessments were added for many participants, including the addition of additional CU validation data at test 3 ( N = 126) and 4 ( N = 279). T‐tests compared observed and predicted differences and residual z‐scores between development and validation at each follow‐up assessment. Result Table 1 presents demographic variables. No additional variables increased the r‐squared by >1%. The final models are presented in Table 2. Age, baseline score, sex, and education were significant. Residual z‐scores were not significantly different between CU development and CU validation cohorts, indicating observed scores were in line with model predicted scores. Residual z‐scores were significantly worse among MCI/DEM compared to CU development in 11/12 comparisons (Figure 1), indicating observed scores were below model predicted expectations and implying sensitivity to longitudinal cognitive decline. Conclusion These longitudinal normative models can be used to identify individuals with follow‐up scores outside of expected predicted ranges, signifying abnormal longitudinal cognitive decline.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

ABSTRACT Introduction Hepatocellular carcinoma (HCC) development in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD) is a growing health concern, but the underlying mechanisms are not fully understood. Epigenetic aging biomarkers, reflecting cellular and tissue aging, have been linked to various age‐related pathologies, but their association with MASLD‐HCC is unknown. We investigated associations between five epigenetic aging biomarkers and MASLD‐HCC risk. Methods We performed whole blood DNA methylation assay (Infinium 850k array) and calculated principal components‐based (PC) versions of HorvathAge, HannumAge, PhenoAge and GrimAge and the DunedinPACE aging rate. We further calculated relative age accelerations for PCHorvathAge, PCHannumAge, PCPhenoAge and PCGrimAge. The aging biomarkers were modelled as continuous variables and categorised into tertiles based on distributions among controls. Associations between each aging biomarker and MASLD‐HCC were examined using logistic regression, calculating odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for covariates. Results Data on 272 MASLD‐HCC cases and 316 cancer‐free MASLD controls recruited from six sites and matched on chronological age, sex and study site were analysed. Higher relative age accelerations of PCPhenoAge (OR T3 vs. T1 = 2.25, 95% CI: 1.45–3.50; OR continuous = 1.04, 95% CI: 1.02–1.07, p = 0.009), PCGrimAge (OR T3 vs. T1 = 3.97, 95% CI: 2.41–6.64; OR continuous = 1.16, 95% CI: 1.10–1.24, p = 8.76 × 10 −07 ) and DunedinPACE (OR T3 vs. T1 = 3.45, 95% CI: 2.17–5.55; OR continuous = 1.72, 95% CI: 1.43–2.10, p = 2.58 × 10 −08 ) were associated with MASLD‐HCC, but not PCHorvathAge or PCHannumAge. Conclusion Higher relative age accelerations of PCPhenoAge, PCGrimAge and DunedinPACE aging rate are associated with risk of MASLD‐HCC. These aging biomarkers could improve HCC risk assessment and facilitate risk stratification in patients with MASLD.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, increasingly arising in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Epigenetic dysregulation, particularly DNA methylation, has been implicated in MASLD-HCC development, yet the roles that the principal DNA methylation precursor metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), play in this association are unclear. Objective: We investigated associations of circulating SAM, SAH, the SAM/SAH ratio, with MASLD-HCC. Methods: In a multi-center pilot case–control study, we evaluated 69 MASLD-HCC cases and 136 cancer-free MASLD controls. Plasma SAM and SAH levels were quantified by liquid chromatography–tandem mass spectrometry. Metabolite levels were categorized as greater than or less than the median based on distribution in controls. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, body mass index, smoking status, and type 2 diabetes. Results: MASLD-HCC cases had significantly higher plasma SAM levels (mean 121 vs. 96 nmol/L; p = 0.001) and SAM/SAH ratios (2.09 vs. 1.48; p = 6.42 × 10−7) than MASLD controls. In multivariable-adjusted models, elevated SAM levels (OR≥median vs. <median = 2.76; 95% CI: 1.38–5.72) and higher SAM/SAH ratio (OR≥median vs. <median = 2.30; 95% CI: 1.15–4.73) were associated with higher odds of MASLD-HCC. SAH alone was associated with MASLD-HCC. Conclusions: Higher plasma SAM levels and SAM/SAH ratios are independently linked to MASLD-HCC development. These metabolites might serve as noninvasive markers for HCC risk stratification in patients with MASLD and improve early detection efforts for MASLD-HCC.

Abstract Background: Epidemiologic studies commonly obtain important exposure and outcome information from patient self-report although these may be influenced by imperfect understanding and/or faulty recall. The Voyage Study is a large-scale, prospective cohort study of long-term health outcomes among participants with a valid multi-target stool DNA (mt-sDNA) test order (N=150, 047). The purpose of this analysis was to evaluate the validity of participant reported mt-sDNA tests, colorectal cancer (CRC), and/or aerodigestive cancers (ADC, including cancers of the head, neck, oral, esophageal, lung, liver, stomach, pancreas, anus) among respondents. Methods: Participants from across the Unites States enrolled in the study completing questionnaires at enrollment and 1 year. The year 1 questionnaire collected data on completion and outcome of their mt-sDNA test, follow up colonoscopies and other procedures as well as a diagnosis of CRC or ADC. The study team contacts those who reported either CRC or ADC for permission to obtain copies of medical records. Records are then reviewed by a trained nurse abstractor and evaluated for validity. Mt-sDNA test results were available from Exact Sciences. Results: At the time of this analysis, year 1 questionnaire data were available on 135, 035 participants. Abnormal mt-sDNA results (n=12, 928) were self-reported by 13, 420 participants with 96.3% accuracy. Of the 111, 556 self-reported normal results, 93.4% (n=104, 150) were reported correctly. The validation process for the Voyage Study is ongoing with 860 self-reported CRC and 318 self-reported aerodigestive cancer diagnoses to date. Complete data are available on 155 individuals with a self-report of a new CRC diagnosis and 63 individuals with a self-report of aerodigestive cancer. Abstraction confirmed 47% (n=73) of those reported as true CRC diagnoses. Participants were better at correctly reporting at least one aerodigestive cancer (73%, n=46). Discussion and Conclusions: Patient reported medical record validation poses a unique challenge in today’s world. The process of requesting participant authorization followed by institutional requests to release the medical records to the research team is slow and painstaking. We found that patients are able to correctly report their mt-sDNA tests but found a relatively low proportion of reported cancer diagnoses reported correctly. This underscores the need to continue to validate key outcomes rather than relying solely on participant responses. As the number of outcomes validated increases, the study team will look at a variety of social and economic factors to better understand what contributes to or hinders an accurate self-report. Citation Format: Emily J. Kirsch, Jessica A. Grimm, Shawn M. Stoddard, Winnie Z. Fan, Kandace A. Bohn, Kathleen J. Yost, Lila J. Finney Rutten, Janet E. Olson. Patient reported outcome validation within the Voyage Study cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7354.

AbstractBackgroundMayo Test DRIVE (MTD): Test Development through Rapid Iteration, Validation and Expansion, is a web‐based multi‐device (smartphone, tablet, personal computer) platform optimized for remote self‐administered cognitive assessment that includes a computer‐adaptive word list memory test (Stricker Learning Span; SLS), a measure of processing speed/executive functioning (Symbols Test) and a score combining both the memory and processing speed/executive functioning measures (MTD Composite). We aimed to determine the reliability of MTD measures across follow‐up sessions.MethodIndividuals participating in this ancillary study who completed at least two and up to four complete MTD sessions were included, resulting in 1,293 participants (mean age = 70.6, SD = 11.6; mean education = 15.6, SD = 2.3; 48.6% male; 96.9% White; 99.1% non‐Hispanic; 97.1% cognitively unimpaired) from parent studies (see Table 1). MTD raw scores were winsorized at 1% and 99% to minimize the impact of outliers. Test‐retest reliability was assessed using single‐rating, absolute‐agreement, and two‐way mixed ICCs and results are described using descriptives from Koo et al. (2016). ICCs for in‐person‐administered traditional neuropsychological measures are also reported alongside MTD for a subset of participants newly enrolled into the MCSA (to ensure participants were test naïve to both MTD and in‐person tests).ResultReliability was good for the MTD Composite raw [total ICC = 0.78; see Table 2 for session‐to‐session ICCs and 95% CIs], and moderate‐to‐good for the primary outcome variables for each subtest [total ICC = 0.74 for both SLS and Symbols]. Weighting by accuracy negatively impacted reliability of Symbols and the Composite; without this weighting, MTD Composite and Symbols variables showed higher reliability (MTD Composite z total ICC = 0.80; Symbols response time total ICC = 0.84; see Table 2). Alternative methods of defining response time on Symbols showed nearly identical reliability values. SLS sum of trials (primary variable) had subtly higher reliability than separate measures of learning (1‐5 correct, max span) or delay. Reliability of remote MTD and in‐person‐administered measures and select comparisons are provided in Table 3.ConclusionMTD showed moderate to good reliability, similar to in‐person‐administered traditional neuropsychological tests. Future work will examine the effect of demographics, device type, and session interference on reliability.