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Background: Functional hypothalamic amenorrhea (FHA) is a common form of secondary amenorrhea caused partly by undernutrition relative to demand. Objective: To compare the dietary nutritional intake between women with FHA and eumenorrheic controls and evaluate micronutrient profiles. Methods: This cross-sectional study included 30 women with FHA and 29 eumenorrheic controls not on hormones. FHA was defined as ≥3 consecutive months of amenorrhea, estradiol of <50 pg/mL, and FSH and LH of <10 mIU/L, excluding other etiologies. A 3-day food diary was collected for dietary analysis, excluding dietary supplements using ESHA Research Food Processor. Results: The mean age of women with FHA and controls was 26.4 years ± 6.2 versus 30.3 years ± 3.7 ( p = 0.002), with no differences in BMI ( p = 0.16) or the waist-to-hip ratio ( p = 0.71). Women with FHA and controls consumed comparable median calorie intake (1784 kcal [1465, 2011] vs. 1732 kcal [1578, 1982], p = 0.62), with no differences in self-reported exercise patterns. Women with FHA consumed more dietary protein (84.2g [74, 112] vs. 74 g [59, 92], p = 0.001) and dietary fiber (30.1 g [22, 43] vs. 17.3 g [15, 23], p = 0.02) versus controls, respectively. When expressed as a percentage of estimated average requirements (% estimated average requirement), women with FHA had higher intakes of vitamin A (105% [45, 219] vs. 43% [21, 86], p = 0.005), vitamin C (201% [139, 351] vs. 107% [75, 190], p = 0.004), and iron (169% [146, 220] vs. 120% [99, 192], p = 0.027) compared with controls. Conclusions: Women with FHA had similar caloric intake to eumenorrheic controls but consumed more dietary protein, fiber, vitamin A, vitamin C, and iron. Future studies should examine the nutritional profiles of women with FHA in relation to long-term health consequences such as bone and vascular health.

Objectives: Cerebral hemodynamic impairment in patients with asymptomatic high-grade internal carotid artery (ICA) stenosis confers a risk of stroke and cognitive decline, but correlations between degree of stenosis and hemodynamic impairment are derived from small case series only. Using baseline data from 282 participants in the Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis – Hemodynamics (CREST-H) study, we hypothesized that degree of stenosis, measured by peak systolic velocity (PSV) and end diastolic velocity (EDV) in the ipsilateral ICA, would correlate with time-to-peak (TTP) delay using MR (n=275) or CT (n=7) perfusion scans, adjusting for demographic and cardiovascular risk variables. Methods: Using a 7-10MHz Doppler probe, ICA PSV and EDV were assessed. Doppler results were adjudicated centrally. Dynamic contrast perfusion imaging was standardized across 61 CREST-H sites, using sequential T2*-weighted time-sequence scanning (e.g. TR=1500, TE=25ms, flip angle=60deg, phase=128, slice thickness=5mm for GE 3T magnet). CTP was acquired with standard clinical protocols. TTP delay was calculated in the ipsilateral versus contralateral MCA/ACA territories using OLEA software. Linear regression used TTP delay as the outcome variable and PSV and EDV as predictor variables, adjusting for covariates.The 282 with complete data for all variables were included in the analysis. Results: Among 392 CREST-H participants, 63% were men, 7% Black, 3% Hispanic, 33% with less than college education. Tables 1a/b show the regression results for PSV and EDV as main predictor variables. PSV and EDV on the index side significantly correlated with TTP delay (p=0.004, p=0.002 respectively). Neither PSV nor EDV on the non-index side correlated with TTP delay (p=0.49, p=0.82). Compared with moderate exercise, vigorous exercise correlated with lower TTP delay in both models (p=0.05. p=0.04). Compared with college graduates, those with HS education had longer TTP delays in both models (p=0.03. p=0.02). Conclusions: In this large cohort of patients with high-grade asymptomatic carotid stenosis, higher flow velocities correlated with greater TTP delays, suggesting an increasing hemodynamic impairment with greater degree of stenosis above 70%. Our findings have implications for management of asymptomatic carotid stenosis which are being tested in the CREST-H and CREST-2 studies. Exercise and education may independently impact cerebral hemodynamics.

BACKGROUND: Cerebral hemodynamic impairment in patients with asymptomatic high-grade (>70%) internal carotid artery (ICA) stenosis is associated with risk of stroke and cognitive decline, but correlations between the degree of stenosis and hemodynamic impairment are derived from small case series. Using baseline data from 242 participants in the CREST-H study (Carotid Revascularization and Medical Management for Asymptomatic Carotid Stenosis–Hemodynamics), we hypothesized that the degree of stenosis in the ICA would correlate with time-to-peak (TTP) delay in ipsilateral cerebral blood flow using MR/CT perfusion scans, adjusting for demographic and cardiovascular risk variables. METHODS: From all participants, ICAs were insonated using a 7 to 10 MHz probe for peak systolic velocity (PSV) and end diastolic velocity. Dynamic contrast perfusion imaging was standardized across 61 CREST-H sites, using standard sequential T2*-weighted perfusion imaging. CT perfusion used standard clinical protocols. TTP delay was calculated in the ipsilateral versus contralateral hemispheres. In cross-sectional analysis, linear regression was used to model TTP delay as the outcome variable and PSV, end diastolic velocity, and PSV ICA/common carotid artery ratio on the index and nonindex side as primary predictor variables in 6 separate models, adjusting for covariates, followed by automated backward elimination model reduction. RESULTS: Among 392 CREST-H participants, the 242 with complete data for all variables were included in the regression analysis (age, 70±7.6; 62% M). End diastolic velocity on the index side correlated with TTP delay (β=0.003, P =0.005). PSV had a similar correlation but did not reach significance (β=0.001, P =0.099), nor did ICA/common carotid artery ratio (β=0.003, P =0.126). Nonindex side PSV, end diastolic velocity, and ICA/common carotid artery ratio showed no correlation with TTP delay ( P =0.268, P =0.495, P =0.380, respectively). Circle of Willis completeness did not correlate with TTP. CONCLUSIONS: In this large cohort of patients with high-grade asymptomatic carotid stenosis, higher end-diastolic flow velocities correlated with greater TTP delays, supporting a definition of hemodynamically significant stenosis. Our findings have implications for the management of asymptomatic carotid stenosis, which will be tested in the CREST-H study. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03121209.

ABSTRACT Objective This study compared the effects of phentermine‐topiramate‐ER (mid‐dose 7.5/46 mg) versus placebo on weight loss and cardiovascular disease (CVD) risk outcomes when used as an adjunct to a digitally enhanced lifestyle intervention (DELI). Methods We conducted a 12‐month, randomized, double‐blind, placebo‐controlled trial at a single tertiary academic center in the United States (June 2020–June 2022). Eighty participants with obesity (BMI ≥ 30 kg/m 2 ) were enrolled in the DELI program, consisting of in‐person and telehealth modalities, dietary and physical activity goals, and use of a smartphone application integrated with digital devices (Apple Watch and Bluetooth‐enabled weight scale and blood pressure monitor). Participants were randomized 1:1 to receive either phentermine‐topiramate‐ER ( n = 42) or placebo ( n = 38) in addition to the DELI. Results At 3 months, the phentermine‐topiramate group lost a mean of 10.82 kg versus 4.04 kg in the placebo group (mean difference −6.78 kg; p = 0.002). At 12 months, weight loss was 15.32 kg versus 5.85 kg, respectively (mean difference −9.48 kg; p < 0.001). Participants receiving phentermine‐topiramate‐ER experienced a 3.35% reduction in the estimated atherosclerotic CVD risk compared to baseline ( p = 0.004). Conclusions Phentermine‐topiramate‐ER, when combined with a DELI, produced significant and sustained weight loss and reduced CVD risk in adults with obesity. Trial Registration ClinicalTrials.gov : NCT04408586

Background/Objectives: Individuals with type 2 diabetes (T2D) achieve less total body weight loss (TBWL) with obesity medications compared to those without T2D. The individualized metabolic surgery (IMS) score, originally developed to predict T2D remission after bariatric surgery, inversely correlates with TBWL response to semaglutide. IMS reflects T2D severity, incorporating HbA1c and T2D duration and medication use. This study aims to evaluate TBWL with tirzepatide across IMS severity categories and identify predictors of response in a real-world cohort. Methods: This retrospective analysis included 717 adults with T2D using tirzepatide for overweight or obesity within the Mayo Clinic Health System. Patients were stratified by IMS severity (mild, moderate, severe) and quartiles. Primary endpoint: TBWL% at 15 months. Secondary endpoints: categorical thresholds (≥5%, ≥10%, ≥15%, ≥20%) and predictors of TBWL%. Linear mixed-effects models and regression models were employed. Results: At 15 months, TBWL was greater in mild versus severe IMS groups (14.8% vs. 11.0%, p = 0.015), with similar trends across quartiles. The proportion achieving ≥ 20% TBWL was nearly two-fold higher in mild versus severe IMS (27% vs. 14%, p = 0.03). Female sex independently predicted greater TBWL, whereas insulin use, higher T2D medication burden (particularly weight-promoting agents), and HbA1c > 7% were associated with less TBWL. Conclusions: Tirzepatide produced clinically meaningful TBWL across all IMS categories, although TBWL declined with increasing IMS severity. Glycemic control and T2D medication use emerged as strong predictors of TBWL. The IMS score may serve as a practical tool to anticipate weight-loss trajectories, guide personalized treatment decisions, and inform patient counseling.

Abstract Background Teclistamab (Tec) and Talquetamab (Tal) are the two first FDA approved bispecific antibodies (BsAb) for the treatment of patients (pts) with relapsed/refractory multiple myeloma (RRMM). Both Tec and Tal should be initiated with step-up dosing (SUD) in an inpatient (IP) setting to mitigate the risk of cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), per US label. However, standard of care practice continues to evolve and more pts receive SUD as an outpatient (OP) to reduce healthcare resource utilization and improve pt convenience. While most real-world evidence has focused on clinical and safety outcomes of pts treated with Tec and Tal, pt experience associated with these novel BsAb therapies is relatively unexplored. Methods HealthTree Foundation, a multiple myeloma pt advocacy group which provides enrolled pts with information on treatments, community groups, and research participation, surveyed RRMM patients who have received Tec or Tal. Eligible pts who completed the treatment experience survey had at least 4 months of follow-up after starting BsAb. The survey was fielded from May 2024 through June 2025. Data collection followed IRB-exempt protocols with informed consent. Results A total of 32 patients (24 Tec, 8 Tal) completed the survey. The median age was 70 years, 50% were male, and 84% were white. Most pts (n=24; 75%) received SUD at their primary oncology clinic (n=19, 59% at an academic center; n=5, 16% at local community center), and 8 (25%) at an academic center which was not their primary oncology clinic. All pts who received SUD at an academic center that was not their primary oncology clinic later transitioned to their local community clinic post-SUD and reported being satisfied or very satisfied with their transition of care experience. Overall, most pts (n= 21; 66%) received their entire step-up dosing during a single hospitalization, while some (n=6; 19%) received all doses of the SUD during separate hospitalizations for each dose. About 13% (n=4) received all SUD in an OP setting, and 3% (n=1) received SUD in a hybrid setting (mix of IP and OP). Although 85% (n=27) of the pts received IP SUD, 66% (n=21) would have preferred IP SUD in one hospitalization, while the remaining 34% (n=11) would have preferred OP or hybrid SUD. Among pts who received IP SUD (n=27), almost a quarter reported a preference for either OP (n=4; 15%) or hybrid SUD (n=2; 7%) and 78% (n=21) preferred one IP hospitalization for SUD. The factors influencing their preference for the setting of SUD in this IP sub-group were feeling more secure with IP monitoring (78%), convenience (26%), less worry about staying near healthcare centers (15%), being able to stay with their local physician (11%), staying closer to home (7%), and lower costs to the pt (4%). Among pts receiving OP SUD (n=4), 75% (n=3) preferred fully OP SUD and 25% (n=1) preferred hybrid SUD because of convenience (n=2; 50%), lower cost to pt (n=1; 25%), flexible dosing schedule (n=1; 25%), and feeling of security with IP monitoring (n=1; 25%). All the pts receiving hybrid SUD (n=1) preferred fully OP SUD due to convenience. Although Tec and Tal are subcutaneous (SC) injections, when patients were asked to compare the options of BsAbs with comparable efficacy and safety profile, they favored SC injection (n= 22; 69%) over intravenous infusion (n = 3; 9%), while 22% (n=7) had no preference for mode of drug administration. The challenges that impacted pt experience with Tec or Tal were treatment side effects (Tal pts: n=6, 75%; Tec pts: n=9, 38%) and distance or travel time to the treatment facility (Tal pts: n=2, 25%; Tec pts: n=4; 17%). Other reasons included caring for others, continuing to work, cost and insurance, lack of caregiver/care partner, being away from home, unavailability of the therapy in community setting near home, and eligibility criteria. Conclusions Most pts in this study received SUD at their primary oncology clinic. While most pts received IP SUD and preferred a single planned IP visit, one third preferred OP or hybrid SUD due to convenience. As the field moves toward OP-only SUD, this study underscores the need to consider pt preferences. There is an unmet need for community clinics providing SUD options closer to pt homes.

Abstract Background: Social determinants of health (SDOH) are critical drivers of cancer care access and outcomes. In multiple myeloma (MM), access to cellular therapies including autologous stem cell transplantation and CAR-T therapy often depends on formal or informal caregiver support. Traditional SDOH measures (e.g., race, income, or education) are often used as proxies for access to care; however, these measures may overlook individual realities pertaining to cellular therapies where a caregiver is generally required. In response to feedback from a HealthTree Patient Advisory Board questioning the practice of inferring caregiver support, this study evaluated a simple survey question by a large cross-sectional survey. Methods: After development of a dedicated single-question (“What best describes your current caregiving support?”) through iterative feedback from the Patient Advisory Board and MM experts, four key answers were settled on: (1) cohabitation with dedicated caregiver(s) to support care (“family support”); (2) presence of non-cohabiting caregiver(s) to provide dedicated support (“external support”); (3) lack of dedicated caregiver, hence support is arranged when needed (“arrangeable support ”); or (4) lack of dedicated caregiver, hence finding any support is a struggle (“inadequate support”). We deployed this new demographic question from February through July 2025 through HealthTree. Responses were analyzed across SDOH variables using Fisher exact or chi square tests, and random forest classifiers were employed for predictive modeling. Results: Of 1231 respondents, 71% had dedicated caregivers (68%, n=839, with family support, and 3%, n=41, with external support). In contrast, 23% (n=287) of patients routinely arranged support while 5% (n=64) had inadequate support (struggle to find support). The inadequate-support group had significant demographic differences from the sample as a whole: a higher proportion were women (78% vs 56%, p = 0.04), Black (9% vs 4%, p &lt; 0.01), lived &gt;50 miles from their treatment center (17% vs 6%, p &lt; 0.01), and had an annual income &lt;$50,000 (17% vs 6%, p = 0.02). This group had a numerically higher proportion of patients whose employment status was disabled (12% vs 5%, p=0.08) as well. No proportion difference in age (67 ± 9 yr), military status, or severity of diagnosis - precursor disease (17%) v. multiple myeloma (81%). Rates of inadequate support were overall low in patients who lived with only their spouse (1%) or lived with their spouse & children (3%). In contrast, rates of inadequate support were higher (than 5%) in patients who lived alone (21%), divorced (20%), lived with adult children (14%), lived with friends (13%), separated (11%), or widowed (9%) (p &lt; 0.001). However, the arrangeable-support group comprised 12% of patients who lived only with their spouse, 19% of patients who lived with their spouse and children, and 61% of patients who lived with their adult children (p &lt; 0.001). Finally, a random forest classifier failed to identify any individual in the inadequate-support category, suggesting that unmeasured or more complex patterns may underlie this classification. Conclusions: To our knowledge, this is the largest analysis of caregiver support and its relationship with other SDOH in patients with MM to date. Despite the importance of caregiving to transplantation and CAR-T therapy, we found that 5% of patients with MM struggle to find caregiver support; this figure likely underestimates the true prevalence, given potential selection bias among patients who are digitally engaged through the HealthTree platform. Even among patients who lived with their spouses, an additional 10-20% of patients routinely had to arrange caregiving support on a visit-by-visit basis; for patients who lived with their adult children, this proportion was over 60%. The requirement to arrange caregiving support can create time toxicity and financial toxicity for patients, although these factors were not directly queried in our study. Finally, caregiver availability cannot be routinely inferred from traditional SDOH variables. Our data highlight the importance of including caregiver status as a standard demographic question in clinical and research settings. Further interventions are needed to improve caregiver support for vulnerable patients with MM as well as to reduce the duration of caregiver requirements for procedures such as CAR-T therapy.

Abstract Introduction Detecting genetic abnormalities is mandatory during diagnostic workup and for potential individualization of therapy selection in multiple myeloma (MM) and its precursor conditions. At present, this requires invasive bone marrow (BM) biopsies, severely limiting early detection, patient monitoring, and the precise selection of therapy. The current standard for detecting genetic alterations in MM is fluorescence in situ hybridization (FISH), which cannot detect point mutations and other clinically relevant alterations. Consequently, the IMS-IMWG guidelines were recently updated to require next-generation sequencing for the classification of high-risk MM. Moreover, the importance of identifying alterations in therapeutic targets (e.g., BCMA, GPRC5D) for guiding immunotherapies is becoming increasingly recognized. To address this need, we recently launched GenoPredicta, a CLIA-approved Laboratory Developed Test that enables routine monitoring, informing diagnosis, and treatment selection by comprehensively and robustly characterizing MM genomes with whole genome sequencing (WGS) from a minimum of ~50 circulating tumor cells (CTCs) isolated from peripheral blood (PB) or tumor cells from BM. Here, we describe the characterization of clinical and research samples with GenoPredicta, highlighting resistance-conferring genetic alterations that can only be detected by sequencing and that are consistent with patients' clinical histories, while further demonstrating the complete concordance of GenoPredicta with prior FISH results. Methods Tumor cells were isolated from BM and/or PB samples using fluorescence-activated cell sorting (FACS) (after CD138+ enrichment for PB) and subjected to whole genome sequencing (WGS), from which we identified copy number alterations, structural variants, and single nucleotide variants and indels. For a subset of samples, the BM was also characterized by FISH, using a standard clinical panel for characterizing MM; for other samples prior FISH results were available from patients' clinical records. Results The analytical validation of GenoPredicta, conducted on a retrospective cohort of &gt;60 viably frozen samples from &gt;30 patients, showed complete concordance between BM and PB for detected genomic abnormalities, with all clinically relevant events (11 translocations and 125 CNVs) observed in BM recapitulated in PB (100% recall). All events identified by FISH in 14 samples (8 translocations and 30 CNVs) were also identified in the BM (100% recall), with 50 translocations and 30 CNVs not detected by FISH also absent in the WGS data (100% precision). The abnormalities detected in these samples also included multiple high-risk events not assayed by FISH, including biallelic TP53 loss (Del 17p coupled with a deleterious TP53 mutation, observed in 4 patients) and MYC::IGL translocations (2 patients). Moreover, for all fresh peripheral blood clinical samples with FISH results available from patients' histories, GenoPredicta recalled 100% of reported events. Among these clinical samples, we notably observed a case with both a biallelic GPRC5D deletion and a TNFRSF17 (BCMA) deletion combined with a truncating mutation on the remaining allele. This was consistent with the patient's clinical history showing progression after therapy with talquetamab (anti-GPRC5D bispecific), and previous nonresponse to teclistamab (anti-BCMA bispecific) after prior therapy with Cilta-cel (BCMA-directed CAR T). GenoPredicta also detected other mutations likely to confer resistance to immunotherapies, including loss-of-function mutations in IKZF3 and deletions of FCRL5 in frozen peripheral blood research samples. Conclusion We demonstrate that WGS-based characterization of MM from BM or CTCs is a viable replacement for FISH for clinical diagnosis and monitoring, with blood-based measurements enabling comprehensive profiling of the myeloma cancer genome. Importantly, this includes genetic alterations that confer resistance to therapy, allowing for both early detection of such alterations and more precise selection of therapy.

Abstract Background Accurate reconstruction of electronic health record (EHR) treatment sequences is essential for leveraging real-world data (RWD) in clinical research and trial-eligibility platforms in multiple myeloma (MM). However, inconsistencies in RWD treatment documentation and the complexity of line-of-treatment (LOT) assignments in MM hinder reproducibility and limit comparisons against published trials. To address these limitations, we developed HEAL-MM, an automated rule-based algorithm that systematically parses structured medication data to calculate LOTs and segment treatment into standardized, clinically meaningful phases. The algorithm is designed to be interpretable, adaptable to evolving therapeutic frameworks, and compatible with widely adopted EHR data standards. Here, we report its real-world validation against expert-adjudicated treatment sequences to assess concordance, identify edge-case failure points, and support RWD integration. Methods A stratified cohort of 100 MM patients (9,136 structured treatment events) was curated from 114 unique healthcare systems compiled from the multi-institutional HealthTree Foundation harmonized registry. Cohort selection was randomized to ensure coverage across diagnostic eras (pre-2010, 2015–2020, 2022–2025) and treatment exposures, including CAR T-cell therapies, bispecific antibodies, and anti-CD38-based regimens. HEAL-MM sequenced MM regimens chronologically and applied predefined logic rules to identify LOT transitions, the initiation of a new therapeutic course based on drug changes, treatment gaps, and procedural anchors such as stem cell transplant or CAR T. Within each LOT, the algorithm segments and labels therapy into phases, clinically distinct intervals (e.g., induction and maintenance) using rule-based clinical logic. Outputs were benchmarked against expert-labeled adjudications. Concordance was evaluated using concordance index (C-index), stratified by patient, LOT depth and diagnostic era. Results: HEAL-MM demonstrated high concordance with expert-labeled treatment sequences: 0.94 for LOT identification, 0.98 for treatment phase segmentation, and 0.96 for phase label assignment. Achieving complete agreement with all expert annotations in 90% of patients for LOT assignment, 94% for the number of treatment phases identified, 77% for the specific labels applied to each phase, and 75% across all three dimensions simultaneously. Among discordant patients, the absolute difference in final LOT count was 0.5 ± 0.9. Performance was highest in early lines (LOTs 1–3; C-index: 0.97), with seven patients showing discrepancies at this stage. Concordance declined in later lines (LOT ≥4; C-index: 0.87), where 50% of the discordant cases (n=4) were attributable to misclassifications in earlier LOTs, such as medication restarts following &gt;3-month gaps, demonstrating the compounding effect of early errors on longitudinal accuracy. Temporal stratification revealed improved accuracy in post-2020 cases (C-index: 0.94) compared to 2015–2020 cohorts (0.91), reflecting the increasing adoption of structured treatment standards and EHR data quality improvements. To assess HEAL-MM performance at clinically significant junctures, we analyzed treatment transitions that initiated a new LOT or marked a change in phase, achieving C-indices of 0.97 for LOT transitions, 0.97 for treatment phase shifts, and 0.94 for phase label assignment. Conclusions: HEAL-MM provides a validated, scalable solution for standardizing MM treatment structures in RWD environments, as it accurately detects the exact treatment entry of a new LOT 97% of the time. This rule-based framework enables reproducible LOT and phase assignment across heterogeneous EHR systems, supporting real-time cohort identification across institutions and EHR platforms. The algorithm's modular design allows for continuous integration of novel therapeutic categories and evolving treatment logic. While the algorithm maintained high fidelity overall, early-stage misclassifications consistent with increased heterogeneity and documentation ambiguity occasionally propagated downstream, underscoring the compounding effect of upstream errors in longitudinal reconstruction. With its generalizable structure and compatibility with industry-standard data models, HEAL-MM lays the foundation for structured treatment reconstruction in MM across RWD infrastructure and serves as a transferable blueprint for other hematologic malignancies.