Gardner, Raquel C.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen's kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure 1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure 1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure 2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure 2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Background Ten years after the original publication, the Centiloid framework is now broadly used to harmonize amyloid‐PET quantification, facilitate data sharing and comparison across cohorts, and even assist visual interpretation in clinical settings. We evaluated the global implementation of Centiloids by comparing their distribution and corresponding positivity thresholds across cohorts. Methods We gathered data from publicly available cohorts and reached out to investigators across the world to collect cross‐sectional Centiloids, demographic and clinical information, and visual read data. Gaussian mixture models (GMM, k=2) were fitted to Centiloid values for each cohort and cutoffs were calculated as mean + 2SD of the lower Gaussian. When visual reads were available, we determined Centiloid cutoffs that maximized correspondence with visual reads (Cohen’s kappa). Data was combined across cohorts using random effects meta‐analyses. Results As of January 2025, we included 37 cohorts ( n = 41,678 participants) with heterogeneous pipelines, radiotracers, and clinical and demographic characteristics (Table‐1). The low Gaussian peaks ranged from ‐9 to 10CL; the meta‐analysis identified a common peak at 1CL. The second peaks were more heterogeneous (range=38‐102CL; meta‐analysis outcome=64CL). Across cohorts, the proportion of cognitively unimpaired versus impaired participants impacted the position of both peaks, with better separation in cohorts enriched in impaired individuals (Figure‐1C). The meta‐analysis indicated a GMM‐based cutoff of 19CL (95%CI: 16‐21CL, Figure‐1B); subgroup analyses showed no evidence of significant effect between single versus multicenter settings (17 versus 20CL, p = 0.30), MRI‐based or PET‐only processing (18 versus 19CL, p = 0.88), and no evidence of difference across radiotracers (Flutemetamol: 16CL; PIB: 17CL, Flutafuranol: 18CL, Florbetaben: 19CL, Florbetapir: 20CL, p = 0.78). In a subset of 29,496 participants with visual reads available, binary visual reads corresponded well to Centiloids (common kappa=0.86, Figure‐2A). The visual read‐based cutoff of 24CL (95%CI: 21‐27CL, Figure‐2B) maximized correspondence between visual read and quantification and was slightly higher than the GMM‐based cutoff. All meta‐analysis models showed high non‐random heterogeneity (I 2 >80%) across studies, suggesting non‐random differences in peaks and cutoffs. Conclusions Meta‐analysis‐based cutoffs align well with thresholds from the existing literature. High heterogeneity among studies underscores the need to investigate contributing factors, raising concerns about applying common cutoffs.

Abstract Older adults have the highest incidence of traumatic brain injury globally. Accurate blood-based biomarkers are needed to assist with diagnosis of patients across the spectrum of age and time post-injury. Several reports have suggested lower accuracy for blood-based biomarkers in older adults, and there is a paucity of data beyond day-1 post-injury. Our aims were to investigate age-related differences in diagnostic accuracy and 2-week evolution of four leading candidate blood-based traumatic brain injury biomarkers—plasma glial fibrillary acidic protein, ubiquitin carboxy-terminal hydrolase L1, S100 calcium binding protein B and neuron-specific enolase—among participants in the 18-site prospective cohort study Transforming Research And Clinical Knowledge in Traumatic Brain Injury. Day-1 biomarker data were available for 2602 participants including 2151 patients with traumatic brain injury, 242 orthopedic trauma controls and 209 healthy controls. Participants were stratified into 3 age categories (young: 17–39 years, middle-aged: 40–64 years, older: 65–90 years). We investigated age-stratified biomarker levels and biomarker discriminative abilities across three diagnostic groups: head CT-positive/negative; traumatic brain injury/orthopedic controls; and traumatic brain injury/healthy controls. The difference in day-1 glial fibrillary acidic protein, ubiquitin carboxy-terminal hydrolase L1 and neuron-specific enolase levels across most diagnostic groups was significantly smaller for older versus younger adults, resulting in a narrower range within which a traumatic brain injury diagnosis may be discriminated in older adults. Despite this, day-1 glial fibrillary acidic protein had good to excellent performance across all age-categories for discriminating all three diagnostic groups (area under the curve 0.84–0.96; lower limit of 95% confidence intervals all >0.78). Day-1 S100 calcium-binding protein B and ubiquitin carboxy-terminal hydrolase L1 showed good discrimination of CT-positive versus negative only among adults under age 40 years within 6 hours of injury. Longitudinal blood-based biomarker data were available for 522 hospitalized patients with traumatic brain injury and 24 hospitalized orthopaedic controls. Glial fibrillary acidic protein levels maintained good to excellent discrimination across diagnostic groups until day 3 post-injury irrespective of age, until day 5 post-injury among middle-aged or younger patients and until week 2 post-injury among young patients only. In conclusion, the blood-based glial fibrillary acidic protein assay tested here has good to excellent performance across all age-categories for discriminating key traumatic brain injury diagnostic groups to at least 3 days post-injury in this trauma centre cohort. The addition of a blood-based diagnostic to the evaluation of traumatic brain injury, including geriatric traumatic brain injury, has potential to streamline diagnosis.

Glial fibrillary acidic protein (GFAP) is cleared by the Food and Drug Administration (FDA) to determine need for head computed tomography (CT) within 12 h after mild traumatic brain injury (TBI) (Glasgow Coma Score [GCS] 13–15); S100 calcium-binding protein B (S100B) serves this function in Europe. This phase 1 biomarker cohort analysis of the multi-center, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study compares GFAP's diagnostic performance, measured on a rapid point-of-care platform, against protein S100B to predict intracranial abnormalities on CT within 24 h post-injury across the spectrum of TBI (GCS 3–15). Head CT scan performed in TBI subjects and blood was collected for all consenting subjects presenting to 18 United States level 1 trauma centers. Plasma was analyzed on a point-of-care device prototype assay for GFAP and serum was analyzed for S100B. In 1359 patients with TBI (GCS 3–15), mean (standard deviation [SD]) age = 40.1 (17.0) years; 68% were male. Plasma GFAP levels were significantly higher in CT+ TBI subjects (median = 1358 pg/mL, interquartile range [IQR]: 472–3803) than in CT- TBI subjects (median = 116 pg/mL, IQR: 26–397) or orthopedic trauma controls ( n = 122; median = 13 pg/mL, IQR: 7–20), p < 0.001. Serum S100B levels were likewise higher in CT+ TBI subjects (median = 0.17 μg/L, IQR: 0.09–0.38) than in CT- TBI subjects (median = 0.10 μg/L, IQR: 0.06–0.18), p < 0.001. Receiver operating characteristic curves were generated for prediction of intracranial injury on admission CT scan; area under the curve (AUC) for GFAP was significantly higher than for S100B in the same cohort (GFAP AUC – 0.85, 95% confidence interval [CI] 0.83–0.87; S100B AUC – 0.67, 95% CI 0.64-0.70; p < 0.001). GFAP, measured on a point-of-care platform prototype assay, has high discriminative ability to predict intracranial abnormalities on CT scan in patients with TBI across the full injury spectrum of GCS 3–15 through 24 h post-injury. GFAP substantially outperforms S100B.