Citations

Introduction: Frontotemporal lobar degeneration (FTLD) is a devastating disease that commonly results in early onset dementia, yet its rarity and heterogeneity limit large-scale research and clinical trials. Remote monitoring via low-burden digital health tools may overcome these barriers. We evaluated clinical utility of passive smartphone monitoring in FTLD using a multi-domain mobile assessment platform. Methods: Participants were 567 adults (53% clinically normal, 21% prodromal FTLD, 26% symptomatic FTLD) who completed an in-person study visit and downloaded the ALLFTD Mobile App on their personal smartphones. The app delivered unsupervised cognitive tests and passively collected continuous data on battery percentage (proxy for smartphone use) and step count. Longitudinal follow-up included smartphone monitoring and annual in-person study visits. Primary analyses examined associations between passive smartphone features and markers of disease severity at baseline and longitudinally, and tested whether passive features added incremental value beyond app-based cognitive testing. Results: Passive smartphone features were feasible to collect and showed excellent reliability with <2 weeks of monitoring (ICCs>0.9). Features capturing smartphone use and movement demonstrated sensitivity to gold-standard clinical measures of disease severity both at baseline and longitudinally. A classification model combining passive features, the app-based cognitive composite score, and demographics detected longitudinal functional decline (AUC=0.89); passive features alone (AUC=0.84) performed comparably to a cognitive screener (AUC=0.85). Discussion: Passive smartphone monitoring is a valid, zero-burden measure of disease severity and progression in FTLD that adds modest incremental value beyond app-based cognitive testing. Findings support its use as a scalable digital endpoint in longitudinal FTLD research.

ABSTRACT Key performance indicators (KPIs) are quantifiable measures that describe critical success factors of an organization. Their vital role in ensuring the delivery of quality health care services necessitates the development of KPIs relevant to inpatient clinical pharmacy practice. This opinion paper from the American College of Clinical Pharmacy (ACCP) Clinical Administration Practice and Research Network (CADM PRN) describes the rationale, methodology, and outcomes of a consensus process to identify a focused set of inpatient/acute care clinical pharmacy KPIs. Leveraging a modified Delphi approach, the CADM PRN convened an Expert Panel of clinical pharmacy leader members to build a consensus‐driven set of KPIs reflecting clinical, operational, patient‐centered, and financial contributions of clinical pharmacists in the inpatient/acute care setting. From a previous ACCP White Paper, a total of 15 KPIs were identified across four domains: (1) clinical practice and patient safety, (2) clinical outcomes and readmissions, (3) medication education and patient engagement, and (4) financial and operational value. The CADM PRN posits that the establishment and implementation of standardized KPIs are essential for measuring and demonstrating the value of clinical pharmacy services in modern health care, offers pragmatic, consensus‐based KPIs for evaluating clinical pharmacy services supporting resource justification, and advancing the visibility and impact of pharmacists in acute care practice. As hospitals shift toward value‐based care, robust KPI frameworks will be critical for optimizing patient outcomes, supporting financial sustainability, and justifying appropriate staffing. Future efforts should focus on pilot testing, addressing implementation barriers, and achieving broader acceptance for KPIs across the profession.

Abstract Genetic studies have significantly advanced our understanding of tauopathies, yet the genetic aetiology of Pick’s disease, a rare 3-Repeat tauopathy, remains unclear. The MAPT p.A152T variant has been identified as a risk factor for Alzheimer’s disease and progressive supranuclear palsy, but its role in Pick’s disease is unknown. In this study, we examined the prevalence of MAPT p.A152T in the largest series of neuropathologically confirmed Pick’s disease cases to date (n = 401). Through genotyping, we identified a single mutation carrier in the Pick’s disease cohort (minor allele frequency = 0.12%). We previously reported MAPT p.A152T at a 0.20% frequency in healthy controls (n = 2456), suggesting that it does not associate with 3-Repeat tauopathy risk. To further investigate the effect of the variant on MAPT transcript expression, we used bulk RNA sequencing in Alzheimer’s disease and progressive supranuclear palsy A152T mutation carriers. We did not detect significant differences in 4-Repeat tau levels, though preliminary trends may indicate more nuanced effects that need to be examined with long-read sequencing in a larger series. Overall, our study suggests that MAPT p.A152T does not increase Pick’s disease risk and may instead be linked to 4-Repeat or mixed tau pathologies, warranting further functional investigation.

Importance TMEM106B is a frontotemporal lobar degeneration (FTLD) genetic susceptibility factor, and TMEM106B protein aggregates are a feature of aging and neurodegeneration. Whether TMEM106B protein levels are associated with clinical features is unknown. Objective To investigate the clinical associations of cerebrospinal fluid (CSF) TMEM106B in FTLD. Design, Setting, and Participants This cross-sectional study was conducted in 2 independent frontotemporal dementia (FTD) cohorts (recruitment from April 2009 through July 2023, with analyses from January 2025 through April 2026), with a 2-year follow up. This multicenter clinical study integrated clinical, genetic, biomarker, and neuroimaging data. Individuals were recruited through the University of California, San Francisco (n = 3733), or ALLFTD (n = 2343). Participants with available CSF were included. A discovery cohort (n = 271) included participants with sporadic neuropathology-confirmed FTLD; presymptomatic or symptomatic carriers of pathogenic variants in C9orf72 , GRN , or MAPT ; or controls. An independent validation cohort (n = 383) included participants with clinically diagnosed sporadic FTD, Alzheimer disease (AD), and controls. Exposures CSF samples for TMEM106B quantification with aptamer proteomics (SomaScan version 3.0 [discovery cohort] and SomaScan version 4.1 [validation cohort]). Main Outcomes and Measures Parametric tests compared the primary outcome, CSF TMEM106B, by disease severity, TMEM106B rs1990622 genotype, sex, clinical syndrome, pathological diagnosis, and pathogenic variant and determined associations with brain volume. Results In the discovery (n = 271; 136 women [51%]; median [IQR] age, 59 [38-80] years) and validation (n = 383; 183 women [48%]; median [IQR] age, 64 [50-78] years) cohorts, lower CSF TMEM106B was associated with more severe disease (β, −0.15; 95% CI, −0.24 to −0.04; P = .003), lower frontotemporal brain volumes (β, 0.42; 95% CI, 0.24-0.61; P &amp;lt; .001), and faster clinical progression (β, −2.21; 95% CI, −3.70 to −0.72; P = .001). Associations of TMEM106B with clinical disease severity were independent of those with neurofilament light chain. TMEM106B levels were influenced by TMEM106B rs1990622 genotype, where individuals with the protective G/G genotype had lower levels than the risk A/A genotype. CSF TMEM106B levels did not differentiate between FTLD subtypes or between FTLD and AD. Conclusions and Relevance Per the results of this cross-sectional study, TMEM106B is detectable in CSF and levels reflect disease severity in sporadic and genetic FTLD and AD, but levels are also influenced by the TMEM106B rs1990622 genotype. CSF TMEM106B could support further studies to understand the mechanisms of disease and develop clinical tools in FTLD and other neurodegenerative diseases.

Abstract Introduction: Early relapse limits durable remissions in multiple myeloma (MM). Cell-free DNA (cfDNA) fragment patterns capture tumor burden and nucleosome positioning. We tested whether a fragmentomics signature trained at MRD time points stratifies progression-free survival (PFS) when applied to pretreatment plasma. Methods: We performed 30-40× whole-genome sequencing of plasma cfDNA from 43 newly diagnosed MM patients, with 65 additional MRD timepoint samples from 41 of those patients (post-ASCT n=30; 1-year maintenance n=30; other maintenance n=5) across eight Canadian sites (TFRIM4 and IMMAGINE studies) and one U.S. site (Mayo SPORE). MRD testing used multiparameter flow cytometry for all follow-up samples (n=65) and clonoSEQ in a subset (n=31). Logistic and elastic-net models were trained at MRD timepoints on fragmentomics features (short-fragment burden; a combined model integrating fragment-length distributions with inferred nucleosome positioning at MM-specific chromatin accessibility regions [Ordoñez et al., Genome Res. 2020]). A decision threshold was fixed by the Youden index during training and applied unchanged to diagnosis samples. PFS was analyzed by Kaplan-Meier, log-rank, and Cox models. Results: After a median follow-up of 49.1 months, 16 of 43 patients (37%) progressed. Using the prespecified locked threshold, patients classified as MRD+ by the proportion-of-short-fragments model had significantly shorter PFS than MRD- patients (HR = 5.12; 95% CI 1.16-22.57; log-rank p = 0.016), with median PFS of 49.2 months for MRD+ cases versus not reached for the MRD- group. Fixed-horizon PFS favored MRD- samples at 24/36/48 months (100/94/87% vs 85/81/53% for MRD+). A combined model integrating weighted fragment lengths with inferred nucleosome positioning also separated outcomes (log-rank p=0.054); the Cox HR was not estimable because no MRD- patients relapsed. At 24, 36, and 48 months, baseline MRD positivity by the short-fragment model identified 100%, 83%, and 86% of patients who relapsed within those intervals (specificity 44%, 43%, and 52%), while the combined model detected all early relapses across the same horizons (specificity 15-21%), indicating consistently high sensitivity but modest specificity for predicting early progression. Conclusions: A cfDNA fragmentomics signature trained at MRD time points stratified PFS when applied to diagnostic plasma, identifying patients at risk of early relapse. Short-fragment burden was most discriminative, and a combined model with nucleosome positioning recapitulated it with higher sensitivity, albeit lower specificity. These data support cfDNA fragmentomics as a practical liquid-biopsy risk biomarker in MM and suggest higher signals may proxy aggressive disease with greater tumor shedding and chromatin disorganization. Future work will validate findings in larger cohorts and integrate cytogenetic and immune features. Citation Format: Dor David Abelman, Jenna Eagles, Aimee Wong, Saumil Shah, Jeffrey Bruce, Stephanie Pedersen, David S. Scott, Cecilia Bonolo de Campos, Signy Chow, Darrell White, Irwindeed Sandhu, Kevin Song, Esteban Braggio, Shaji Kunnathu Kumar, Alli Murugesan, Tony Reiman, A. Keith Stewart, Suzanne Trudel, Trevor J. Pugh. Baseline cfDNA fragmentomics identifies multiple myeloma patients at risk of early progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2597.

ABSTRACT Interactions between clinical pharmacists and industry partners continue to evolve as health care increases in complexity and technology assumes a more dominant role. To address the corresponding ethical challenges, the 2025 Industry Relations Committee of the American College of Clinical Pharmacy (ACCP) was charged with updating the 2008 ACCP position statement, Pharmacists and Industry: Guidelines for Ethical Interactions . This update expands its perspective, reflecting the broader and more influential roles pharmacists play across the health care ecosystem. All clinical pharmacist–industry interactions should be grounded in improving patient care. Through this lens, six recommendations are provided: (1) Pharmacists should not engage in industry partnerships or collaborations that might unduly influence objectivity, independence, or fairness in clinical and professional judgments; (2) Pharmacists should disclose and mitigate financial, consulting, or other relationships that constitute potential conflicts of interest and should not participate in activities where an existing conflict cannot be resolved; (3) Pharmacists who participate in industry‐sponsored research should follow accepted ethical, regulatory, and scientific standards; (4) Pharmacists providing continuing education programs or developing drug information materials should maintain control of content; ensure their content is fair, balanced, and free from bias; and disclose and mitigate any conflicts; (5) Formal instruction on ethical interactions with industry professionals should begin early and continue through all stages of pharmacist education, training, and professional development; (6) Pharmacists should maintain the confidentiality and privacy of patients and other health care professionals when working with industry partners. Questions to guide personal industry interactions are provided. This paper does not detail every emerging area (e.g., artificial intelligence, social media), but recommendations are applicable wherever clinical pharmacists leverage their professional expertise. By maintaining a critical, discerning, and ethical approach to industry collaborations, clinical pharmacists can uphold professional obligations while contributing to the advancement of evidence‐based practice, ultimately prioritizing patient welfare.

Abstract BACKGROUND: Obesity is prevalent in Hispanic patients (pts) with breast cancer (BC), but this population is often underrepresented in studies of weight loss interventions (WLIs). The BWEL trial (Alliance A011401; NCT02750826) is a phase III randomized trial testing the impact of a 2-year (yr) telephone-based WLI on invasive disease-free survival in pts with stage II-III HER2-negative BC and a body mass index (BMI) &gt; 27 kg/m2. At 12 months (mo), Hispanic pts lost less weight than White pts on the WLI. Here, we evaluate differences in intervention engagement over the first 12 mo by language preference and ethnicity among BWEL pts on the WLI. METHODS: BWEL randomized pts 1:1 to WLI + health education (HE) or HE alone. WLI +HE pts received semi-structured telephone-based health coaching in English or Spanish, in addition to access to an activity monitor, wireless scale and meal replacement shakes. Pts self-reported ethnicity and their preferred language for the WLI. Kruskal Wallis and chi-square tests were used to assess language and ethnicity differences in demographic and treatment factors for continuous and categorical variables, respectively. Percent weight change and mean values for intervention attrition, number of calls, call duration, number of shake shipments, and frequency of activity monitor use and weight tracking over 12 mo were compared between Spanish-preferred Hispanic pts (SH), English-preferred Hispanic (EH) pts and non-Hispanic (NH) pts using ANOVA. Pts with missing language, ethnicity or 12 mo weight data were excluded from analysis. RESULTS: 1591 pts were randomized to the WLI + HE from 08/2018 to 02/2021. 80.5% of pts were White, 12.8% Black and 7.1% Hispanic. 1190 pts had 12 mo weight and language available. Among the 87 WLI + HE pts who had available data and identified as Hispanic, 34% were SH and 66% EH. SH WLI pts were younger (in yrs: 49.5 SH, 51.1 EH, 53.9 NH, p=0.006) and had lower BMI (32.2 SH, 35.1 EH, 34.5 NH, p=0.007). At 12 mo, SH WLI pts lost 2.4% of baseline body weight vs 3.3% in EH and 5.1% in NH (p=0.051) (Table). Compared with EH and NH WLI pts, SH WLI pts participated in fewer calls (15.7 SH, 21.1 EH, 24.3 NH, p&lt; 0.0001), had fewer days of activity monitor use (134.2 SH, 177.3 EH, 205.3 NH, p=0.015) and fewer days of weight tracking (65.3 SH, 96.8 EH, 148.2 NH, p&lt;0.0001), but had more shake shipments (3.9 SH, 3.5 EH, 3.0 NH, p=0.015). There were no significant differences in attrition rate or call duration by language preference or ethnicity. CONCLUSIONS: In a large phase III WLI study in breast cancer patients, engagement with the WLI appeared lower in SH pts, despite provision of the intervention in Spanish. More work is needed to improve accessibility and efficacy of WLI studies among non-English speaking populations. Support: U10CA180821, U10CA180882, UG1CA189823; https://acknowledgments.alliancefound.org. Citation Format: A. Odai-Afotey, K. V. Ballman, L. M. McCall, C. Cao, C. Alfano, V. Bernstein, T. E. Crane, L. M. Delahanty, L. Frank, P. J. Goodwin, O. Hahn, D. L. Hershman, J. O. Hopkins, M. Irwin, E. L. Mayer, L. Minasian, L. Nebeling, M. Neuhouser, E. D. Paskett, P. A. Spears, V. Stearns, C. A. Thomson, T. A. Wadden, A. Weiss, J. White, C. Hudis, E. P. Winer, L. Carey, A. H. Partridge, J. Ligibel. Variation in weight loss and intervention engagement in the Breast Cancer Weight Loss (BWEL) trial by language and ethnicity (Alliance) [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-09-29.

Abstract Background: Obesity is a poor prognostic factor in early breast cancer (BC). The BWEL trial (Alliance A011401; NCT02750826) is a phase 3 randomized trial testing the impact of a weight loss intervention (WLI) on invasive disease-free survival in women with stage II-III BC and a body mass index (BMI) ≥27 kg/m2. Here we report trajectories of weight change in BWEL WLI participants (pts). Methods: Eligible pts were within 16 months of diagnosis of stage II-III HER2-negative BC, had completed chemotherapy (chemo) and radiation (if given), and were randomized 1:1 to a telephone-based WLI, focused on caloric restriction and exercise, plus health education (HE) or to an HE-alone control group. WLI pts were assigned a trained coach at the BWEL call center and received 42 phone calls over the 2-year (yr) WLI period. Weight and height at baseline, 6 and 24 months were collected by study staff. All WLI pts were provided with a Fitbit wireless scale. Fitbit weight records (FWRs) were automatically imported to the BWEL portal for pts with wireless access and self-reported in the portal for pts without wireless access. FWRs were averaged (avg) by week (wk) from the WLI start date up to 104 wks. Baseline pt factors were self-reported; treatment history was abstracted from the medical record. Linear mixed-effect models were used to evaluate trajectories of weight change over the 104-wk WLI. Principal component analysis and multinomial models were used to examine distinct clusters of weight trajectories. Results: 3180 pts were enrolled from 637 sites between 8/2016 and 2/2021. FWRs were available from 1508 of 1591 WLI pts, with a total of 366,294 FWRs collected during the 2-yr WLI period. In pts with FWRs, mean baseline BMI was 34.4 ± 5.6 kg/m2, mean age was 53.2 ± 10.3 yrs, 10.8% self-identified as Non-Hispanic (NH) Black and 6.1% as Hispanic, 81.1% received chemo and 56.9% were postmenopausal. No statistically significant differences in baseline factors were observed between pts with and without FWRs. Intraclass correlation coefficient of baseline weights collected by study staff and by Fitbit scales was 0.99. In the WLI cohort, avg weekly weight (kg) decreased from 91.4 (95% CI: 90.6-92.3) at baseline to 85.6 (95% CI: 84.8-86.5) by wk 40, corresponding to a peak loss of 6.1% (95% CI: 5.7-6.4) of baseline weight. Weight remained stable until wk 52 and then gradually increased to 87.0 kg (95% CI: 86.2-87.9) at wk 104, corresponding to a 4.4% (95% CI: 4.0-4.8) loss at wk 104 vs. baseline. Principal component analysis identified 3 distinct weight trajectories: 67.6% of pts experienced peak weight loss of 6.6% (95% CI: 6.3-6.9) of baseline weight, 10.0% experienced peak loss of 17.5% (95% CI: 16.8-18.2) of baseline weight, and 22.4% did not achieve weight loss, gaining on avg 2.2% (95% CI: 1.7-2.6) of baseline weight. Pts with college (relative risk ratio [RRR]=1.41, 95% CI: 1.03-1.92) and post-graduate education (RRR= 1.58, 95% CI: 1.13-2.20) and those who were premenopausal (RRR=1.70, 95% CI: 1.28-2.26) were more likely to be in the cluster that did not lose weight. Pts who were NH White (RRR=3.60, 95% CI: 1.95-6.68), postmenopausal (RRR=1.94, 95% CI: 1.28-2.95), had no alcohol use (RRR=1.49, 95% CI: 1.03-2.16), had not received chemo (RRR=1.76, 95% CI: 1.15-2.72), and had &lt;3 chronic conditions (RRR: 2.12, 95% CI: 1.23-3.66) were more likely to be in the cluster with the highest weight loss. Conclusion: Longitudinal weight data from BWEL demonstrate peak weight loss of 6.1% in the WLI cohort at 40 wks, with distinct trajectories indicating differential responses to the WLI. This work demonstrates the feasibility of using digital health tools to track outcomes in the setting of a large-scale weight loss intervention study and informs future precision behavioral interventions in oncology. Support: U10CA180821, U10CA180882, UG1CA189823; U10CA180863. https://acknowledgments.alliancefound.org. Citation Format: C. Cao, K. Ballman, L. McCall, C. Alfano, V. Bernstein, T. Crane, L. Delahanty, L. Frank, P. Goodwin, O. Hahn, D. Hershman, J. Hopkins, M. Irwin, E. Mayer, L. Minasian, L. Nebeling, M. Neuhouser, E. Paskett, P. Spears, V. Stearns, C. Thomson, T. Wadden, A. Weiss, J. White, C. Hudis, E. Winer, A. Partridge, L. Carey, J. Ligibel. Weight Trajectories in the Breast Cancer Weight Loss (BWEL) Trial (Alliance) [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PD8-01.