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Background: Despite recent major advances, multiple myeloma (MM) remains incurable using therapies that are currently available to patients. There are no available/approved targeted therapies for MM that allow for individualized therapeutic approaches to the disease. Fifteen to 20% of newly diagnosed MM patients have a translocation of chromosomes 4 and 14 [t(4;14)]. This translocation puts transcription of the gene encoding Multiple Myeloma SET domain-containing protein (MMSET or NSD2), a histone 3 lysine 36 methyltransferase, under the control of the IgH super enhancer. The super enhancer drives the overexpression of MMSET resulting in an abnormal histone code that promotes myelomagenesis. Presence of t(4;14) is associated with poor clinical prognosis (Caro Am Soc Clin Oncol Educ Book 2021). KTX-1001 is a first-in-class, selective and potent small molecule inhibitor of MMSET, and is the first investigational drug that targets the t(4;14) MM patient population. Methods: This Phase 1, first-in-human open-label study (NCT05651932) is designed to assess the safety, tolerability, and initial clinical activity of KTX-1001 in relapsed/refractory MM (RRMM) and has two parts: dose escalation (Part A) and dose expansion (Part B). In Part B, only t(4;14)+ MM patients will be evaluated in single agent and combination expansion cohorts. Patients with RRMM who have received ≥3 prior lines of therapy, including a proteasome inhibitor (PI), immunomodulatory drug (IMiD), and anti-CD38 antibody are eligible for the dose escalation part. The primary objective of the study is to determine a recommended phase 2 dose (RP2D) and schedule of KTX-1001. Secondary and exploratory objectives include pharmacokinetic (PK) and pharmacodynamic (PD) evaluation of KTX-1001, and assessment of preliminary efficacy. KTX-1001 is administered orally until disease progression, unacceptable toxicity, or patient withdrawal from the study. A Safety Review Committee (SRC) reviews all data prior to each dose escalation decision and on a regular basis. Results: As of June 14, 2024, 17 patients (pts) have been treated with KTX-1001 in Part A across 6 dose levels (SRC reviewed). Dose Level 7 has been reached with no dose limiting toxicities (DLTs). Ten of these patients are t(4;14) positive. Patients have received a median of 5 prior lines of therapy (range: 3-17), including 7 pts with prior BCMA CAR-T, 6 pts with prior BCMA bispecific and/or ADC, 5 pts with prior non-BCMA bispecific, and 12 pts with prior transplant. Patient demographics include a median age of 68 years (range: 50-83), 10 male/ 7 female, 13 white, 2 black or African American, 2 as other identified race, and 3 Hispanic or Latino pts. Six pts remain on treatment. KTX-1001 shows excellent tolerability to date, with treatment emergent AEs (TEAEs) (CTCAEv5.0) predominantly of grade 1 and 2 severity. Most frequently observed TEAEs suspected to be related to KTX-1001 include fatigue (35%), diarrhea (24%), and constipation (24%), for which no grade 3 or 4 events have been observed. Frequency and/or severity of TEAEs have not increased with escalating doses nor treatment duration, and no patients have discontinued due to an AE. Two heavily pre-treated patients [one known to be t(4;14)+] have demonstrated long-lasting stable disease and clinical benefit at 10 and 7 months respectively. Both patients have been intra-patient dose escalated. For PK and PD data collected to date, drug exposure and steady-state concentration of KTX-1001 (at C1D15, when steady state concentration has been reached) appear to be dose-proportional. PD data indicate a dose-dependent decrease in the H3K36me2 mark on treatment versus baseline across dose levels 1 through 6, suggesting that the RP2D may be reached by year end. Updated results will be presented at the congress. Conclusions: Dose escalation of KTX-1001 is ongoing with an excellent tolerability profile to date and demonstration of on-target pharmacodynamics that will inform the determination of the RP2D(s). These PD data support the targeted mechanism of action of KTX-1001 to selectively inhibit MMSET and break the t(4;14) histone code. Given KTX-1001's promising safety profile, single-agent and combination expansion cohorts with standard-of-care MM agents/classes will begin in 2025 to further optimize doses and provide proof of concept in Part B of the Phase 1 study.

Background: Philadelphia (Ph)-like acute lymphoblastic leukemia (ALL) is a heterogenous subtype of B-ALL defined by the presence of gene expression profiling similar to Ph+ ALL in the absence of a BCR::ABL1 fusion. Ph-like ALL has lower response rates to standard frontline therapies and carries a poorer prognosis when compared to other B-ALL subtypes. However, the role in which specific patient characteristics may impact responses is not well described. Our aim for this retrospective study was to evaluate the characteristics and outcomes of a real-world cohort of patients (pts) with Ph-like ALL across multiple institutions, including describing key patient characteristics, treatments, and factors that may contribute to minimal residual disease (MRD) status and treatment response. Methods: We conducted a retrospective review of adult pts with Ph-like ALL diagnosed and treated at the University of New Mexico, Mayo Clinic Phoenix, University of Chicago, and University of Michigan between 1/1/18 - 2/1/2024. Pts were eligible if they carried a diagnosis of B-ALL, and were determined to have Ph-like ALL according to each institutional diagnostic algorithm. Descriptive statistics, ANOVA, and Fisher's exact test were employed to summarize various clinical characteristics and their associations. Cox regression analysis was utilized to estimate the median survival time and assess the relationship between clinical characteristics and survival outcomes, including overall survival and progression-free survival. All statistical analyses were conducted using the R statistical software. Results: 67 pts were identified with a median age of 34.2 (IQR 28.5) and 35.8% were female. Twenty-eight pts identified as Hispanic or Latino (H/L) (43.1%), 25 (38.5%) identified as non-Hispanic White/Caucasian (NHW), 10 (15.4%) as American Indian/Alaska Native (AI/AN), and 2 (3.1%) as Asian/Pacific Islander. Thirteen pts (19.4%) had central nervous system (CNS) involvement at diagnosis, 22.4% of pts had extramedullary disease, and, in those who had NGS or cytogenomic microarray testing (n=49), 51% had an identified mutation/deletion in IKZF1. Initial treatment varied widely with pts receiving most commonly: Children's Oncology Group (COG) protocols (22.4%), CALGB 10403 (22.4%), HyperCVAD (11.9%), and CALGB 8811 (10.4%). Fifty-eight percent achieved a CR/CRi and 21% were refractory to initial therapy. Thirteen percent of pts received a Jak inhibitor and 6% of pts were treated with a TKI. Thirty pts (50.8%) underwent allogeneic stem cell transplant, with 8 of those pts (26.7%) experiencing a relapse post-transplant. Nine pts (13.4%) underwent treatment with CAR-T therapy with few pts experiencing grade 2 or higher cytokine release syndrome (CRS) or immune effector cell-associated neurotoxicity syndrome (ICANS). Pts with CNS disease at diagnosis were less likely to achieve a CR/CRi compared to those without CNS disease at diagnosis (33.3% vs 64.0%; p=0.12). Pts identifying as AI/AN were more likely to be refractory to their first treatment compared to other racial/ethnic groups (75%, p=0.002). Forty two percent of pts never achieved MRD negativity (< 0.01% by flow cytometry) during treatment; pts identifying as H/L and AI/AN had a higher rate of MRD positivity after induction compared to NHW pts (56% and 70% vs. 25%; p=0.02). In pts with IKZF1 mutation, 57.1% did not achieve MRD negativity compared to 21.7% in pts without a mutation (p=0.029). The median overall survival (OS) was 66.3 months (mos) and the median progression-free survival (PFS) was 15.7 mos. Pts who did not achieve MRD negativity after initial treatment had shorter OS compared to those who did achieve MRD negativity, 33.9 mos vs 66.3 mos (p=0.086). Progression-free survival (PFS) was significantly shorter for patients who did not achieve MRD negativity after initial treatment compared to those who did, 3.9 mos vs 43.9 mos (p=0.001). PFS was shorter in pts who identify as AI/AN compared to H/L and NHW pts (2 mos vs 35.7 mos and 35.7 mos; p=0.046). Conclusion: Pts with Ph-like ALL experience poor response rates to therapies used to treat B-ALL, highlighting the urgent need for novel therapies and treatment strategies. We identified race/ethnicity and CNS disease at diagnosis as risk factors for poor treatment response. In addition, race/ethnicity and the presence of an IKZF1 mutation was associated with worse rates of MRD negativity during treatment.

Background: The term classical hematology (CH), referring to a subspecialty focusing on non-cancer blood disorders, was officially adopted by the American Society of Hematology (ASH) in 2022. There is a perceived shortage of classical hematologists, although data are limited (Winter JN, Hematologist 2022). Prior studies have shown that <6% of hematology-oncology fellows pursue a practice subspecializing in CH after training (Todd RF, Blood 2004; Marshall Al, Blood Adv 2018). Moreover, mentorship is consistently cited as the most important factor guiding fellows' career decisions (Sharma D, Blood Adv 2019; Masselink LE, Blood Adv 2019). To help bridge the CH workforce gap, ASH has supported additional hematology-focused fellowship programs (HFFP) within select hematology-oncology fellowship programs. CH constitutes a large proportion of consults in US National Cancer Institute (NCI)-designated cancer centers (West CV, JCO Oncol Pract 2024). The goals of our study were: 1) to determine the proportion of adult hematologist-oncologists within NCI cancer centers who practice CH; and 2) to describe the physician characteristics of cancer centers with HFFP and compare these centers to those without HFFP. Methods: We included all NCI-designated cancer centers with a clinical program as of April 1, 2024. The only exception was St Jude Children's Hospital due to its pediatric focus. We obtained the practice profile of each internal medicine-trained adult hematologist-oncologist (henceforth, physician) as well as the availability of HFFP from the respective cancer center websites. We also searched the corresponding department of medicine and division of hematology at each affiliated academic institution to ensure inclusion of all physicians practicing CH. Each physician was classified into one of the following categories: classical hematologist (attending to only non-cancer conditions), general hematologist-oncologist (cancer and non-cancer conditions), and oncologist (cancer only). The number of hematology-oncology fellowship programs in the US was obtained from the Accreditation Council for Graduate Medical Education. Results: We included 64 NCI-designated cancer centers, 55 of which were considered as comprehensive cancer centers. There was a total of 4,616 physicians (3,891 oncologists [84.3%], 487 hematologists-oncologists [10.6%], and 238 classical hematologists [5.1%]). The median numbers of physicians, oncologists, general hematologist-oncologists, and classical hematologists per center were 55 (range, 16-275), 45 (range, 12-270), 5 (range, 0-48), and 3 (range, 0-13), respectively. The median proportions of classical hematologists and physicians practicing CH (general hematologist-oncologists plus classical hematologists) were 5.1% (range, 0-20.0) and 14.9% (range, 0-55.6%), respectively. There were 12 HFFP within NCI-designated cancer centers (18.8%). Most (11/12) centers with HFFP had >50 physicians (median 95 [range, 38-185]) and >5% classical hematologists (median 6% [range, 3.8-13.5%]); 11 centers with similar characteristics did not have HFFP. Centers with HFFP have significantly higher numbers of physicians (median 95.0 [range, 38-185] vs 49.5 [range, 16-275]; P=0.001) and classical hematologists (median 6.0 [range, 3-13] vs 2.0 [range, 0-11]; P<0.001) but similar proportions of classical hematologists (median 6.0% [range, 3.8-13.5] vs 4.5% [range, 0-20.0]; P=0.078) and physicians practicing CH (median 12.3% [range, 6.5-47.7] vs 15.7% [range, 0-55.6]; P=0.402) compared to those without HFFP. Conclusions: Among adult hematologist-oncologists at NCI-designated cancer centers, only 1 in 20 are classical hematologists, although 1 in 7 have CH as part of their practice. Within NCI-designated cancer centers, only 1 in 5 have an HFFP. These findings suggest that there is an opportunity for NCI-designated cancer centers to encourage and recruit physicians subspecializing in CH and a rationale to create additional HFFPs. These measures may help further alleviate CH workforce shortages and expand CH mentorship to meet the needs of an increasingly complex subspecialty within hematology.

In 2023, Mayo Clinic in Rochester, MN, cared for over 20,000 patients with hematologic malignancies, roughly 42% of whom were rural. Patients residing in rural areas can face challenges in access to care, financial and time burdens due to treatment-related travel, and limited opportunities to participate in clinical trials. In 2021, The Leukemia & Lymphoma Society launched its IMPACT (Influential Medicine Providing Access to Clinical Trials) grant with Mayo Clinic being among its first recipients. The program provides funding to major cancer centers, in partnership with local community oncology centers, to help increase participation of patients traditionally underrepresented in clinical trials, including those who are rural, minority and/or economically disadvantaged. The award allowed significant changes to be made to clinical trial selection and facilitation models increasing access to trials within the rural setting and reducing the socioeconomic barriers noted above. This program included Rochester (IMPACT Hub) and Mayo Clinic Health System (MCHS) sites in Albert Lea, MN (65 miles to Hub), Eau Claire, WI (96 miles to hub), La Crosse, WI (73 miles to Hub), and Mankato, MN (84 miles to Hub). Methods: Two models were deployed; the full activation model where clinical trials were fully opened at MCHS sites, and the hybrid model where clinical trial participants are consented at the Hub and portions of participant care are conducted at MCHS sites. Initial barriers included limitations in clinical trials support staff, insufficient specimen processing equipment, medication storage and shipping limitations, MCHS catchment population unalignment with the Hub trials portfolio, and reduced bandwidth of providers due to patient care responsibilities. To address barriers to trial conduct, community center site champions were identified to streamline trial review and increase collaboration to better define site catchment areas, trial constraints, and site needs. A feasibility specialist was hired to support the program in trial design/implementation, barrier tracking, and to serve as a liaison between community site providers, study staff, and the program. Results: Since 2021, 20 clinical studies including 14 interventional treatment trials have been activated demonstrating 108 total patients accrued with 42 interventional accruals; 5 within the full activation model (13 accruals) and 15 in the hybrid model (29 accruals). Patients showed an average of 216 miles, with an estimated mileage cost of $142/trip based on the 2023 IRS mileage rate of $0.655/mile to the hub location. Nine of those patients traveled over 260 miles, with an estimated mileage cost of $170/visit to access clinical trials. Under the hybrid model, 8 patients transitioned portions of their care to their home city resulting in reduction of an average of 155 miles/visit, and 7 patients were able to reduce their travel time an average of 216 miles/visit. By estimating at least 10 study visits, the travel burden was reduced by 2,160 miles (4320 driving hours) and $1,420 in driving costs alone for participants. Using participant averages the 108 patients would spend a total of $15,336 in travel costs for just the initial visit, but when factoring in the 10-visit estimate, travel reduction could result in a net savings of $153,360. Based on travel data, it is estimated 11 of these patients would have required overnight accommodation using an average of $149/night hotel stay, a savings of $1,490/patient is estimated for the lifecycle of trial participation or $16,390 for the 11-patient cohort requiring accommodation. These results do not include travel companion and time off work costs. Efforts have been made to further develop the full activation model and 5 clinical trials are in activation. Data gathered from the program is being used to design strategies to reduce the administrative burden of clinical trial participation for community providers and launch screening programs. Conclusion: The Mayo Clinic LLS IMPACT program has demonstrated success in addressing barriers to opening clinical trials in the community-center setting and reduced the travel burden and financial impact to clinical trial participation in a cohort of trial participants. Centralized hub support of study activation and conduct and decentralized care to support treatment of patients in proximity of their home can be extended to other hospital systems and settings.

Background The American Society of Clinical Oncology (ASCO) launched the Diversity in Oncology initiative in 2017 to address the need for increased minority representation in the hematology-oncology workforce. A recent series of reports, titled “State of the Oncology Workforce in America,” published in JCO Oncology Practice in 2021 and 2022, focused on this issue. Notably, these reports did not include any data regarding the representation of Asian minorities in the field. Therefore, our objective was to assess the proportion of Asian professionals within the hematology-oncology workforce, encompassing both practitioners and trainees. Methods We conducted a comprehensive search for publicly available data regarding Asian representation among US hematology-oncology practitioners and trainees in both adult and pediatric fields. Our primary data sources included the 2020 US Census, the Association of American Medical Colleges, the Accreditation Council for Graduate Medical Education, and various professional societies. We defined practitioners to include hematologist-oncologists, gynecologic oncologists, radiation oncologists, surgical oncologists, hematopathologists, hematology-oncology nurse practitioners, hematology-oncology physician assistants, and nurses specializing in hematology-oncology. Trainees encompassed fellows and residents. Additionally, we gathered comparable data on the US population, medical students, adult internal medicine residents, gynecology residents, pediatric residents, pathology residents, surgery residents, nurses, nurse practitioners, and physician assistants. To provide a comprehensive overview of the hematology-oncology workforce, we utilized data from 2020 to 2022, and whenever possible, we collected longitudinal data on trainees over the years. Results As of 2020, Asians represented 6.2% of the US population. Among hematology-oncology physician practitioners, the percentage of Asian individuals ranges from 14.7% to 32.1%, including 32.1% of adult hematologist-oncologists, 20.4% of pediatric hematologist-oncologists, 25.9% of radiation oncologists, 14.7% of gynecologic oncologists, and 7.0% of hematology-oncology physician assistants. In the realm of physician trainees, Asian representation is between 17.9% and 35.3%, with 35.3% of adult hematology-oncology fellows, 20.0% of pediatric hematology-oncology fellows, 26.4% of radiation oncology residents, and 17.9% of gynecologic oncology fellows. Additionally, the proportion of Asian residents from which hematology-oncology fellows are drawn ranges from 14.9% to 35.9%. Specifically, 35.9% of internal medicine residents, 22.0% of pediatric residents, 14.9% of gynecology residents, 26.6% of pathology residents, and 19.2% of surgical residents are Asian. Furthermore, 26.7% of medical students identify as Asian. Notably, the proportions of Asian hematology-oncology trainees and medical students have remained relatively stable from 2005 to 2022. Data on hematopathologists, surgical oncologists, and non-physician practitioners are limited, although Asian representation among hematology-oncology physician assistants is 7.0% and among nurses is 7.2%. Conclusions Asians comprise a large segment of the hematology-oncology workforce in the US. While only 1 in 20 people in the US are Asians, between 1 in 3 and 1 in 5 hematology-oncology physician practitioners and trainees, are Asians. Data are limited for non-physician practitioners. Asian minority representation should be highlighted and considered as an example of success in diversity in hematology-oncology workforce. This framework should be embraced moving forward to continue advancing minority representation throughout the field of hematology-oncology.

Abstract Rationale Among tobacco-exposed persons with preserved spirometry (TEPSs), we previously demonstrated that different lung volume indices—specifically, elevated total lung capacity (TLC) versus elevated ratio of functional residual capacity to TLC (FRC/TLC)—identify different lung disease characteristics in the COPDGene cohort. Objective We sought to determine differential disease characteristics and trajectories associated with lung volume indices among TEPSs in the SPIROMICS cohort. Methods We categorized TEPSs (n = 814) by tertiles (low, intermediate, and high) of TLC or residual volume–to-TLC ratio (RV/TLC) derived from baseline computed tomography images and then examined clinical and spirometric disease trajectories in mutually exclusive categories of participants with high TLC without high RV/TLC ([TLC]high) versus high RV/TLC without high TLC ([RV/TLC]high). We examined differences in computed tomography–measured emphysema (Hounsfield units [HU] ⩽−950; parametric response mapping [PRM] of emphysema), air trapping (HU⩽−856; PRM of functional small airway disease; a disease probability measure for non-emphysematous gas trapping), airway geometry (the mean square root of wall area of a hypothetical airway with 10 mm internal perimeter), respiratory symptoms (on the modified Medical Research Council Dyspnea Scale; COPD Assessment Test [CAT]; St. George’s Respiratory Questionnaire [SGRQ]; and Short Form-12 [SF12]), and outcomes (annualized exacerbation rate) between the two categories at baseline and over follow-up time up to 8.5 years, using regression modeling adjusted for age, sex, height, weight, and smoking status (current vs. former smoker) and burden (pack-years). Results In TEPSs, the pattern of spirometric disease progression differed between participants with [TLC]high and those with [RV/TLC]high: There was increased forced vital capacity with stable forced expiratory volume in 1 second in participants with [TLC]high, versus unchanged forced vital capacity but nominally decreased forced expiratory volume in 1 second in those with [RV/TLC]high. Compared with participants with [TLC]high, TEPSs with [RV/TLC]high had less emphysema (by HU ⩽−950) but more airway disease (by HU ⩽−856; PRM of functional small airway disease; disease probability measure for gas trapping, and mean square root of wall area of a hypothetical airway with 10 mm internal perimeter), more respiratory symptoms (on the modified Medical Research Council Dyspnea Scale, CAT, SGRQ, and SF12), and more severe exacerbations at baseline. Over an average follow-up of 4.1 ± 2.4 years (range = 0.5–8.5 yr), TEPSs with [RV/TLC]high also had a higher likelihood of developing more severe spirometric disease (preserved ratio impaired spirometry or Global Initiative for Chronic Obstructive Lung Disease Classification 2) and worsening of their respiratory symptoms (on the CAT and SGRQ). Although the incidence rates of respiratory exacerbations, hospitalizations, and mortality were not significantly different between the two categories over the follow-up period, TEPSs with [RV/TLC]high were more likely to have been prescribed a respiratory inhaler at their last follow-up visit. Conclusions In these TEPSs from the SPIROMICS cohort, lung volume stratification by TLC versus RV/TLC identifies two pre-COPD phenotypes with distinct respiratory symptoms, radiographic features, and clinical trajectories. The characteristics of these pre-COPD phenotypes match those previously described in the COPDGene cohort using TLC versus FRC/TLC stratification.

Background: Recent studies have reported associations between alterations in gut microbiome composition (GMC) and cardiovascular disease risk factors (CVR). However, data regarding the use of GMC as a biomarker of CVR is scarce. Aims: The current study was designed to assess the distinct patterns in GMC among patients with varying degrees of CVR and/or atherosclerotic cardiovascular disease (ASCVD). Methods: Patients with a range of CVR including hypertension (HTN), hyperlipidemia (HLD), diabetes (DM), and/or ASCVD referring to Mayo Clinic from 2013 to 2018 were prospectively enrolled. DNA extracted from stool samples was analyzed using the V3-V5 region of the 16s data. Microbial α-diversity was assessed by the observed taxonomic units, Shannon, and Chao1 indices. β-diversity was assessed using Bray-Curtis dissimilarity and plotted using principal coordinates analysis. Hierarchical clustering was used to identify patterns in the GMC samples. Random Forest analysis was used to identify the most important clinical factors differentiating the clusters. Results: A total of 211 patients with a median age of 60 [IQR: 50,70] years and with 90 (42.7%) males were included. Two clusters of GMC were identified (Figure 1A) . Cluster 1 and 2 had 104 (49.3%) and 107 (50.7%) patients, respectively. Among CVRs, age and body mass index were the most prominent factors contributing to the difference in GMC among clusters ( Figure 1B ). Cluster 2 had a better α diversity profile than Cluster 1 (Figure 1C-E) . There was no significant difference in TMAO between clusters (P=0.6). Cluster 2 patients were younger (P<0.001), leaner (P=0.007), more physically active (P<0.001), less male (P=0.009), and had a lower prevalence of ASCVD (P=0.003), HTN (P=0.010), and HLD (P=0.005). There was no significant difference in the prevalence of DM (P=0.063), smoking (P=0.446), and alcohol intake (P=0.134) between the clusters. Conclusion: This study suggests the potential of GMC profiling as a valuable biomarker for assessing CVRs, with age and BMI as the most prominent factors associated with GMC clustering.

Introduction: Patients with heart failure with preserved ejection fraction (HFpEF) have a high burden of comorbidities. To recognize the complex interplay of obesity, diabetes, and chronic kidney disease (CKD) in patients with cardiovascular disease, the cardiovascular-kidney-metabolic (CKM) syndrome was recently defined, but data on the burden of CKM in HFpEF is limited. Aim: We sought to describe the prevalence of CKM components in HFpEF leveraging data from control participants in clinical trials and NHANES. Methods: We analyzed data presented in publications from four randomized clinical trials that enrolled participants with HFpEF (all of which are available via NHLBI BioLINCC and will be included in the extant HeartShare dataset): TOPCAT, RELAX, NEAT-HFpEF, and INDIE. We abstracted baseline demographic and clinical variables from study publications to define CKM components (e.g., body mass index [BMI], diabetes status, hypertension, CKD status). We compared prevalence of CKM in these trials with a representative sample of US adults with self-reported history of heart failure (HF) from NHANES 2011-2018. Results: We included 1937 patients with HFpEF from four trials with enrollment between 2006-2016 and 715 adults representing 5.6 million adults aged ≥40 years with a self-report of HF between 2011-2018 from NHANES ( Table ). Mean age was similar among enrolled trial participants with HFpEF and those with HF in NHANES (~68-69 years). Mean BMI was in the obesity range and was similar among trial participants with HFpEF and HF in NHANES (31-35 kg/m 2 ). A significant proportion of individuals had hypertension in HFpEF trials (>80%) and in the NHANES sample with HF (89%). The proportion of stage ≥ 3 CKD in HFpEF trials (≥ 37%) was similar to the NHANES sample with HF (37%). Results are shown in Table . Conclusions: Patients with HFpEF enrolled in clinical trials and those with HF in general population are significantly enriched with CKM components that may contribute to morbidity and mortality. Similar prevalence estimates were observed despite variability in trial inclusion criteria and heterogeneity in defining HFpEF and highlight CKM as a central target for HFpEF management.

ABSTRACT Medically important pathogenic fungi invade vertebrate tissue and are considered primary when part of their nature life cycle is associated with an animal host and are usually able to infect immunocompetent hosts. Opportunistic fungal pathogens complete their life cycle in environmental habitats or occur as commensals within or on the vertebrate body, but under certain conditions can thrive upon infecting humans. The extent of host damage in opportunistic infections largely depends on the portal and modality of entry as well as on the host’s immune and metabolic status. Diseases caused by primary pathogens and common opportunists, causing the top approximately 80% of fungal diseases [D. W. Denning, Lancet Infect Dis, 24:e428–e438, 2024, https://doi.org/10.1016/S1473-3099(23)00692-8 ], tend to follow a predictive pattern, while those by occasional opportunists are more variable. For this reason, it is recommended that diseases caused by primary pathogens and the common opportunists are named after the etiologic agent, for example, histoplasmosis and aspergillosis, while this should not be done for occasional opportunists that should be named as [causative fungus] [clinical syndrome], for example, Alternaria alternata cutaneous infection. The addition of a descriptor that identifies the location or clinical type of infection is required, as the general name alone may cover widely different clinical syndromes, for example, “rhinocerebral mucormycosis.” A list of major recommended human and animal disease entities (nomenclature) is provided in alignment with their causative agents. Fungal disease names may encompass several genera of etiologic agents, consequently being less susceptible to taxonomic changes of the causative species, for example, mucormycosis covers numerous mucormycetous molds.