Citations

AbstractObjectivePrion disease classically presents with rapidly progressive dementia, leading to death within months of diagnosis. Advances in diagnostic testing have improved recognition of patients with atypical presentations and protracted disease courses, raising key questions surrounding the relationship between patterns of neurodegeneration and survival. We assessed the contribution of fluorodeoxyglucose (FDG‐PET) imaging for this purpose.MethodsFDG‐PET were performed in 40 clinic patients with prion disease. FDG‐PET images were projected onto latent factors generated in an external dataset to yield patient‐specific eigenvalues. Eigenvalues were input into a clustering algorithm to generate data‐driven clusters, which were compared by survival time.ResultsMedian age at FDG‐PET was 65.3 years (range 23–85). Median time from FDG‐PET to death was 3.7 months (range 0.3–19.0). Four data‐driven clusters were generated, termed “Neocortical” (n = 7), “Transitional” (n = 12), “Temporo‐parietal” (n = 13), and “Deep nuclei” (n = 6). Deep nuclei and transitional clusters had a shorter survival time than the neocortical cluster. Subsequent analyses suggested that this difference was driven by greater hypometabolism of deep nuclei relative to neocortical areas. FDG‐PET‐patterns were not associated with demographic (age and sex) or clinical (CSF total‐tau, 14‐3‐3) variables.InterpretationGreater hypometabolism within deep nuclei relative to neocortical areas associated with more rapid decline in patients with prion disease and vice versa. FDG‐PET informs large‐scale network physiology and may inform the relationship between spreading pathology and survival in patients with prion disease. Future studies should consider whether FDG‐PET may enrich multimodal prion disease prognostication models.

Background: Paroxysmal nocturnal hemoglobinuria (PNH) is a rare, acquired, life-threatening disease characterized by complement-mediated hemolysis. OPERA is the first US longitudinal real-world study on C3 inhibitor therapy, known as pegcetacoplan. Methods: OPERA enrolled US patients with PNH, age ≥18, who were prescribed pegcetacoplan, and data were collected from routine care. Hemoglobin was reported by patients during regular follow-up (censored from transfusions). The Functional Assessment of Chronic Illness Therapy (FACIT)-Fatigue (0–52 score) and Patient-Reported Outcomes Measurement Information System scale for Cognitive Function Abilities (PROMIS-CF; 23.27–67.09 t-score) were completed electronically (low score = negative outcome). Patients self-reported incidence of healthcare resource utilization (HCRU). Results: By January 2024, 70 patients (mean age 44.6 years; 57.1% female) reported up to 9 months of pegcetacoplan treatment, with a median [IQR] follow-up of 6.6 [3.8] months. The latest reported hemoglobin levels improved by a mean (SD) of 2.6 (1.9) g/dL from baseline. At 3, 6 and 9 months, patients reported clinically meaningful improvements (≥5 points) in FACIT-F (53.3–69.0%) and (≥2 points) PROMIS-CF (46.7–55.2%). Patients reported a <10% incidence rate per person month of all HCRU events. Conclusions: This first longitudinal real-world US study indicates a positive trend in Hb, fatigue, and cognition with limited HCRU during pegcetacoplan treatment in adults with PNH.

Abstract Introduction Vulnerable populations experience a higher burden of cardiovascular disease.(1) However, the incidence of cardiovascular risk factors, such as diabetes, within these populations, is incompletely characterized. Recently, the United States Centers for Disease Control and Prevention (US CDC) developed the Social Vulnerability Index (SVI) to categorize counties that may be vulnerable to worse health outcomes. The SVI is composed of 4 themes: socioeconomic status (Theme 1), household composition & disability (Theme 2), minority status & language (Theme 3), and housing type & transportation (Theme 4).(2) We sought to evaluate the association of the 4 SVI themes with county incidence of diagnosed diabetes. Method We used CDC data (2004-2019; reported for 94% of US counties), on county incidence of diagnosed diabetes. The SVI themes were scored from 0 to 1 (low to high vulnerability) based on national percentile ranking. We used weighted least squares regression with restricted cubic splines to model the relationship between SVI themes and incidence of diagnosed diabetes (median data 2004−2019), adjusting for median age, sex ratio, state, percent uninsured, food environment index, healthcare providers, and rurality.(3) The partial effects of SVI themes and diabetes incidence were reported as F-statistics (P-values) and diabetes incidence rate ratios (IRR [95% confidence interval]), which were plotted for all values compared with the 50th percentile (SVI 0.5). For each theme, we also present the IRR for a 0.5-point increase when moving over the lower (SVI score 0.5 vs. 0.0) and the upper (1.0 vs. 0.5) half of the SVI score distribution. Results The 4 SVI theme rank scores were significantly associated with diabetes incidence (all, P&lt;.001). However, Theme 1 and 2 scores showed stronger associations with IRR than Theme 3 and 4 (Figure). For Theme 1, SVI score 0.5 vs. 0.0 was associated with a 15% higher incidence (IRR 1.15 [1.13–1.18]) and score 1.0 vs. 0.5 was associated with a 13% higher incidence (IRR 1.13 [1.11–1.16]). For Theme 2, SVI score 0.5 vs. 0.0 was associated with a 16% higher incidence (IRR 1.16 [1.14–1.18]), but there was no difference between rates for SVI score 1.0 vs. 0.5 (IRR 1.01 [0.99–1.03]). For Theme 3, there was no difference between SVI score 0.5 vs. 0.0 (IRR 0.98 [0.96–1.00]), but rates were 8% lower for SVI score 1.0 vs. 0.5 (IRR 0.92 [0.90–0.94]). For Theme 4, SVI score 0.5 vs. 0.0 was associated with a 3% lower incidence (IRR 0.97 [0.96–0.99]) and score 1.0 vs. 0.5 was associated with a 2% higher incidence (IRR 1.02 [1.01–1.04]). Conclusion In US counties, the strength and directionality of the association between SVI themes and diabetes incidence differed by theme and level of SVI scores. These themes, particularly socioeconomic status and household composition & disability may be leveraged to identify high-risk counties for primary prevention of diabetes, with implications for reduction in cardiovascular risk.