Fever of unknown origin (FUO) is a diagnostic challenge. This study aimed to assess the efficacy of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and gallium-67 single-photon emission computed tomography/computed tomography (67Ga SPECT/CT) in diagnosing FUO. A total of 68 patients with FUO underwent 18F-FDG PET/CT and 67Ga SPECT/CT from January 2013 through May 2016. Images were read independently. The imaging results were compared with the final diagnosis and categorized as helpful for diagnosis or non-contributory to diagnosis in the clinical setting. Associations between categorical variables were evaluated with the chi-square test or Fisher's exact test. Ten of the 68 patients were excluded. An infectious underlying disease was found in 23 patients. A malignant disorder was the cause of FUO in 10 patients. Non-infectious inflammatory disease was found in 11 patients. Adrenal insufficiency was the cause of FUO in two patients. The cause of FUO was not found for 12 patients. A high false-positive rate of 44% (7/16) was observed for 18F-FDG PET/CT, while a high false-negative rate of 55% (23/42) was observed for 67Ga SPECT/CT. 18F-FDG PET/CT studies depicted all 67Ga-avid lesions. The sensitivity (79% vs. 45%) and clinical contribution (72% vs. 55%) of 18F-FDG PET/CT in diagnosing FUO were significantly higher than those of 67Ga SPECT/CT (p<0.05). On the basis of this study, the diagnostic performance of 18F-FDG PET/CT is superior to 67Ga SPECT/CT in the work-up of patients with FUO. With its rapid results and superior sensitivity, 18F-FDG PET/CT may replace 67Ga SPECT/CT where this technique is available.

Alzheimer’s disease (AD) is a complex neurodegenerative disease, and genetic differences may mediate neuronal degeneration. In humans, a single-nucleotide polymorphism in the B-cell chronic lymphocytic leukemia/lymphoma-2 (Bcl-2) gene, rs956572, has been found to significantly modulate Bcl-2 protein expression in the brain. The Bcl-2 AA genotype has been associated with reduced Bcl-2 levels and lower gray matter volume in healthy populations. We hypothesized that different Bcl-2 genotype groups may modulate large-scale brain networks that determine neurobehavioral test scores. Gray matter structural covariance networks (SCNs) were constructed in 104 patients with AD using T1-weighted magnetic resonance imaging with seed-based correlation analysis. The patients were stratified into two genotype groups on the basis of Bcl-2 expression (G carriers, n = 76; A homozygotes, n = 28). Four SCNs characteristic of AD were constructed from seeds in the default mode network, salience network, and executive control network, and cognitive test scores served as the major outcome factor. For the G carriers, influences of the SCNs were observed mostly in the default mode network, of which the peak clusters anchored by the posterior cingulate cortex seed determined the cognitive test scores. In contrast, genetic influences in the A homozygotes were found mainly in the executive control network, and both the dorsolateral prefrontal cortex seed and the interconnected peak clusters were correlated with the clinical scores. Despite a small number of cases, the A homozygotes showed greater covariance strength than the G carriers among all four SCNs. Our results suggest that the Bcl-2 rs956572 polymorphism is associated with different strengths of structural covariance in AD that determine clinical outcomes. The greater covariance strength in the four SCNs shown in the A homozygotes suggests that different Bcl-2 polymorphisms play different modulatory roles.

The amyloid framework forms the central medical theory related to Alzheimer disease (AD), and the in vivo demonstration of amyloid positivity is essential for diagnosing AD. On the basis of a longitudinal cohort design, the study investigated clinical progressive patterns by obtaining cognitive and structural measurements from a group of patients with amnestic mild cognitive impairment (MCI); the measurements were classified by the positivity (Aβ+) or absence (Aβ−) of the amyloid biomarker. We enrolled 185 patients (64 controls, 121 patients with MCI). The patients with MCI were classified into two groups on the basis of their [¹⁸F]flubetaben or [¹⁸F]florbetapir amyloid positron-emission tomography scan (Aβ+ vs. Aβ−, 67 vs. 54 patients) results. Data from annual cognitive measurements and three-dimensional T1 magnetic resonance imaging scans were used for between-group comparisons. To obtain longitudinal cognitive test scores, generalized estimating equations were applied. A linear mixed effects model was used to compare the time effect of cortical thickness degeneration. The cognitive decline trajectory of the Aβ+ group was obvious, whereas the Aβ− and control groups did not exhibit a noticeable decline over time. The group effects of cortical thickness indicated decreased entorhinal cortex in the Aβ+ group and supramarginal gyrus in the Aβ− group. The topology of neurodegeneration in the Aβ− group was emphasized in posterior cortical regions. A comparison of the changes in the Aβ+ and Aβ− groups over time revealed a higher rate of cortical thickness decline in the Aβ+ group than in the Aβ− group in the default mode network. The Aβ+ and Aβ− groups experienced different APOE ε4 effects. For cortical–cognitive correlations, the regions associated with cognitive decline in the Aβ+ group were mainly localized in the perisylvian and anterior cingulate regions. By contrast, the degenerative topography of Aβ− MCI was scattered. The memory learning curves, cognitive decline patterns, and cortical degeneration topographies of the two MCI groups were revealed to be different, suggesting a difference in pathophysiology. Longitudinal analysis may help to differentiate between these two MCI groups if biomarker access is unavailable in clinical settings.

Dietary pattern (DP) results in nutrition adequacy and may influence cognitive decline and cortical atrophy in Alzheimer’s disease (AD). The study explored DP in 248 patients with AD. Two neurobehavioral assessments (intervals 13.4 months) and two cortical thickness measurements derived from magnetic resonance images (intervals 26.5 months) were collected as outcome measures. Reduced rank regression was used to assess the groups of DPs and a linear mixed-effect model to explore the cortical neurodegenerative patterns. At screening, underweight body mass index (BMI) was related to significant higher lipid profile, impaired cognitive function, smaller cortical thickness, lower protein DP factor loading scores and the non-spouse caregiver status. Higher mini-mental state examination (MMSE) scores were related to the DP of coffee/tea, compared to the lipid/sugar or protein DP group. The underweighted-BMI group had faster cortical thickness atrophy in the pregenual and lateral temporal cortex, while the correlations between cortical thickness degeneration and high HbA1C or low B12 and folate levels were localized in the medial and lateral prefrontal cortex. The predictive model suggested that factors related to MMSE score were related to the caregiver status. In conclusion, normal or overweight BMI, coffee/tea DP group and living with a spouse were considered as protective factors for better cognitive outcomes in patients with AD. The influence of glucose, B12 and folate on the cortical degeneration was spatially distinct from the pattern of AD degeneration.

The purpose of this study was to investigate whether plasma biomarkers can help to diagnose, differentiate from Alzheimer disease (AD), and stage cognitive performance in patients with positron emission tomography (PET)-confirmed primary age-related tauopathy, termed tau-first cognitive proteinopathy (TCP) in this study. In this multi-center study, we enrolled 285 subjects with young-onset AD (YOAD; n = 55), late-onset AD (LOAD; n = 96), TCP (n = 44), and cognitively unimpaired controls (CTL; n = 90) and analyzed plasma Aβ42/Aβ40, pTau181, neurofilament light (NFL), and total-tau using single-molecule assays. Amyloid and tau centiloids reflected pathological burden, and hippocampal volume reflected structural integrity. Receiver operating characteristic curves and areas under the curves (AUCs) were used to determine the diagnostic accuracy of plasma biomarkers compared to hippocampal volume and amyloid and tau centiloids. The Mini-Mental State Examination score (MMSE) served as the major cognitive outcome. Logistic stepwise regression was used to assess the overall diagnostic accuracy, combining fluid and structural biomarkers and a stepwise linear regression model for the significant variables for MMSE. For TCP, tau centiloid reached the highest AUC for diagnosis (0.79), while pTau181 could differentiate TCP from YOAD (accuracy 0.775) and LOAD (accuracy 0.806). NFL reflected the clinical dementia rating in TCP, while pTau181 (rho = 0.3487, p = 0.03) and Aβ42/Aβ40 (rho = −0.36, p = 0.02) were significantly correlated with tau centiloid. Hippocampal volume (unstandardized β = 4.99, p = 0.01) outperformed all of the fluid biomarkers in predicting MMSE scores in the TCP group. Our results support the superiority of tau PET to diagnose TCP, pTau181 to differentiate TCP from YOAD or LOAD, and NFL for functional staging.

Purpose Progressive supranuclear palsy (PSP) and Alzheimer’s disease (AD) exhibit different tau distributions. We compared tract-specific associations between florzolotau(18F) retention, white matter (WM) microstructure, and clinical impairment. Methods Forty-nine patients with PSP, 40 with AD, and 30 cognitively unimpaired controls underwent florzolotau(18F) PET. Tractography evaluated projection and association fibers, including the dentatorubrothalamic tract (DRTT) and superior longitudinal fasciculus (SLF). Regional SUVRs were calculated using WM-PERSI normalization. Associations among uptake, diffusion measures, brain volume, and clinical outcomes were evaluated using covariate-adjusted correlations with Bonferroni correction, partial canonical correlation, and exploratory path analyses. Results PSP was characterized by predominantly subcortical and brainstem florzolotau(18F) retention and more extensive WM abnormalities, including DRTT involvement. AD showed predominantly cortical retention and microstructural abnormalities of SLF branches. In PSP, regional subcortical uptake was associated with DRTT microstructure, while DRTT mean diffusivity and frontal volume contributed most strongly to the canonical variate associated with motor impairment (canonical correlation = 0.82, p = 0.001). In AD, cortical uptake was associated with SLF microstructure, while cortical volume and SLF mean diffusivity contributed to the canonical variate associated with executive function (canonical correlation = 0.83, p = 0.001). Exploratory path models were consistent with indirect associations involving white matter microstructure and cortical volume. Conclusion PSP and AD showed distinct patterns of tract vulnerability associated with regional florzolotau(18F) retention and clinical impairment. These cross-sectional findings support further longitudinal investigation of disease-specific network degeneration.