Type 2 diabetes (T2D) is associated with increased risk of Alzheimer's disease (AD). There is evidence for impaired blood–brain barrier (BBB) in both diseases, but its role in the interplay between them is not clear. Here, we investigated the effects of high‐fat diet (HFD), a model for T2D, on the Tg2576 mouse model of AD, in regard to BBB function. We showed that HFD mice had higher weight, more insulin resistance, and higher serum HDL cholesterol levels, primarily in Tg2576 mice, which also had higher brain lipids content. In terms of behavior, Tg2576 HFD mice were less active and more anxious, but had better learning in the Morris Water Maze compared to Tg2576 on regular diet. HFD had no effect on the level of amyloid beta 1–42 in the cortex of Tg2576 mice, but increased the transcription level of insulin receptor in the hippocampus. Tg2576 mice on regular diet demonstrated more BBB disruption at 8 and 12 months accompanied by larger lateral ventricles volume in contrast to Tg2576 HFD mice, whose BBB leakage and ventricular volume were similar to wild‐type (WT) mice. Our results suggest that in AD, HFD may promote better cognitive function through improvements of BBB function and of brain atrophy but not of amyloid beta levels. Lipid metabolism in the CNS and peripheral tissues and brain insulin signaling may underlie this protection.

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We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

Background: Myelodysplastic syndromes (MDS) are diagnosed with a bone marrow examination (BME), an invasive procedure that patients (pts) would rather avoid. Earlier (Oster et al, Leuk Lymph 2018) we developed a logistic regression (LoR) model to diagnose MDS by incorporating 6 variables (age, gender, Hb, WBC, PLT, MCV) into a formula (Figure 1A). We improved the model using data from 178 MDS pts (47 from Tel Aviv, 131 from the EUMDS registry), and 178 controls (ASH 2017). Here we significantly improve the model using a much larger dataset (501 pts; 501 controls) and additional variables. Methods: The EUMDS registry contains data on 2600 BME-proven MDS pts. A random sample of 501 MDS pts from the registry was combined with 501 controls with no MDS (ruled out with BME). Gradient-boosted models (GBM) were used to predict having or not having MDS, using the variables age, gender, Hb, WBC, PLT, MCV, neutrophils, monocytes, glucose, and creatinine (Figure 1B). Area under the ROC curve (AUC), sensitivity and specificity were used to evaluate the models, and model performance was validated by using 100 times 5-fold cross-validation. Model stability was also assessed by repeating the fit of the models using different randomly chosen groups of 501 EUMDS pts as cases. Results: The AUC was 0.97 (95% CI 0.96-0.98, Figure 2), compared with an AUC of 0.87 (0.84-0.91) achieved previously. Under cross-validation, AUC was 89%. Maximizing the sum of sensitivity and specificity led to sensitivity of 88% and specificity of 95%. This means we can calculate a threshold "GBM score," assigning a subject to "MDS" or "no MDS" status with a specificity of 95% and a sensitivity of 88%. Alternatively, we can set two GBM score thresholds G1 & G2, where a GBM score > G2 provides 95% specificity and a score < G1 provides 95% sensitivity. A score between these two cutoffs gives an indeterminate probability of disease. Only 24% of our MDS patients and 15% of our control patients fall into this indeterminate region, compared with about 50% in our earlier model. The most influential variables were MCV, creatinine and neutrophils. Repeated random choice of cases from the EUMDS registry led to stable results. Conclusions: Using easily accessible parameters, MDS can be diagnosed or excluded non-invasively with high accuracy in a substantially large portion of patients. While the Logistic Regression model (Figure 1A) can be used with a relatively simple formula, the Gradient Boosted Model (Figure 1B) is more complex. The GBM combines the variables and the interactions among them, achieving an AUC that represents an excellent predictive ability and a considerable improvement over the previous model. Adding peripheral blood cytogenetic/genetic information could further improve non-invasive MDS diagnosis, and obviate the need for bone marrow examination in many patients. We continue to improve and validate the model. An on-line calculator/app for use in a clinical setting is being developed and will be presented. Disclosures Smith: Jazz Pharmaceuticals: Research Funding; Johnson & Johnson: Research Funding; Novartis: Research Funding; Gilead Sciences: Consultancy. Fenaux:Celgene: Honoraria, Research Funding; Otsuka: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Stauder:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees. Germing:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Janssen: Honoraria.

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IMPORTANCE Previous studies have shown that uniform pathologic review of all splenectomy surgical specimens reveals new clinically actionable diagnoses only in a minority of cases.OBJECTIVE To examine whether the aggregate of clinical, laboratory, imaging, and pathologic preoperative data is associated with a clinically beneficial pathologic study for routine splenectomy surgical specimens. DESIGN, SETTING, AND PARTICIPANTSThis single-center retrospective cohort study included all patients who underwent splenectomy from January 1, 2013, through December 31, 2018, at a single center. Clinical, imaging, and pathologic data were extracted from the institution's electronic medical records system. Data analysis was conducted from June to November 2020. EXPOSURES Undergoing splenectomy for trauma or diagnostic or therapeutic indications. MAIN OUTCOMES AND MEASURES Spleen pathology study resulting in a new medical diagnosis or change in medical management. RESULTS Overall, 90 patients (53 [59%] men) with a median (range) age of 59 (19-90) years underwent splenectomy for therapeutic purposes in 41 patients (45%), trauma in 24 patients (27%), diagnostic purposes in 15 patients (17%), and combined therapeutic and diagnostic purposes in 9 patients (10%). In 14 patients (15%) a new malignant neoplasm was found, and in 8 patients (9%), a new nonneoplastic medical condition was diagnosed. A new pathologic diagnosis resulted in change in medical management in 16 patients (18%). In patients without a prior diagnosis of cancer, 41 of 56 pathology biopsies (73%) were found to be normal whereas in 7 biopsies (13%), a new diagnosis of a hematologic malignant neoplasm was revealed (P < .001). Patients with clinical splenomegaly were significantly more likely to have a new pathologic diagnosis of cancer compared with patients without splenomegaly (15 of 26 [58%] vs 4 of 64 [7%]; P < .001). In 39 of 43 patients (91%) with normal presurgery imaging studies, normal spleen pathology was revealed, whereas in 14 of 17 patients (82%) with abnormal imaging studies, a new hematological malignant neoplasm was diagnosed following pathologic review of the spleen specimen (P < .001). Patients with gross abnormalities on macroscopic examination had a significantly increased likelihood of a hematological cancer diagnosis (17 of 40 [43%]) and a solid cancer diagnosis (4 [10%]) compared with patients with grossly normal specimens (4 of 49 [8%]; P < .001). CONCLUSIONS AND RELEVANCEIn this cohort study, routine pathologic review of spleen specimens was clinically beneficial in patients with splenomegaly, abnormal imaging results, a prior diagnosis of cancer, and with grossly abnormal spleens.