Explainable deep-learning-based ischemia detection using hybrid O-15 H2O perfusion positron emission tomography and computed tomography imaging with clinical data.

BACKGROUND: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 H2O perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings. METHODS: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables. Data from invasive coronary angiography were used as reference. Performance was evaluated with clinical classification using accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE), precision (PRE), net benefit, and Cohen's Kappa. Statistical testing was conducted using McNemar's test. RESULTS: The DL model had a median ACC = 0.8478, AUC = 0.8481, F1S = 0.8293, SEN = 0.8500, SPE = 0.8846, and PRE = 0.8500. Improved detection of true-positive and false-negative cases, increased net benefit in thresholds up to 34%, and comparable Cohen's kappa was seen, reaching similar performance to clinical reading. Statistical testing revealed no significant differences between DL model and clinical reading. CONCLUSIONS: The combined DL model is a feasible and an effective method in detection of CAD, allowing to highlight important data findings individually in interpretable manner.

Hybridizing machine learning in survival analysis of cardiac PET/CT imaging.

BACKGROUND: Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up. METHODS: Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model. RESULTS: Compared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score. CONCLUSION: Prognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation.

Genome-Wide Association Study and Identification of a Protective Missense Variant on Lipoprotein(a) Concentration: Protective Missense Variant on Lipoprotein(a) Concentration-Brief Report.

[Figure: see text].

One-tissue compartment model for myocardial perfusion quantification with N-13 ammonia PET provides matching results: A cross-comparison between Carimas, FlowQuant, and PMOD.

PURPOSE: To cross-compare three software packages (SPs)-Carimas, FlowQuant, and PMOD-to quantify myocardial perfusion at global, regional, and segmental levels. MATERIALS AND METHODS: Stress N-13 ammonia PET scans of 48 patients with HCM were analyzed in three centers using Carimas, FlowQuant, and PMOD. Values agreed if they had an ICC > 0.75 and a difference < 20% of the median across all observers. RESULTS: When using 1TCM on the global level, the agreement was good, and the maximum difference between 1TCM MBF values was 17.2% (ICC = 0.83). On the regional level, the agreement was acceptable except in the LCx region (25.5% difference, ICC = 0.74) between FlowQuant and PMOD. Carimas-1TCM agreed well with PMOD-1TCM and FlowQuant-1TCM. Values obtained with FlowQuant-1TCM had a somewhat lesser agreement with PMOD-1TCM, especially at the segmental level. CONCLUSIONS: The global and regional MBF values (with one exception) agree well between the different software packages. There is significant variability in segmental values, mainly located in the LCx region and segments. Out of the studied tools, Carimas can be used interchangeably with both PMOD and FlowQuant for 1TCM implementation on all levels-global, regional, and segmental.

Multi-task Deep Learning of Myocardial Blood Flow and Cardiovascular Risk Traits from PET Myocardial Perfusion Imaging.

BACKGROUND: Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks. METHODS AND RESULTS: We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR <2.0 ml/g/min) as well as to classify cardiovascular risk traits (factors), namely sex, diabetes, arterial hypertension, dyslipidemia and smoking at the individual-patient level from PET myocardial perfusion polar maps using transfer learning. Performance was assessed on a hold-out test set through the area under receiver operating curve (AUC). DL achieved the highest AUC of 0.94 [0.87-0.98] in classifying an impaired MFR in reserve perfusion polar maps. Fine-tuned DL for the classification of cardiovascular risk factors yielded the highest performance in the identification of sex from stress polar maps (AUC = 0.81 [0.73, 0.88]). Identification of smoking achieved an AUC = 0.71 [0.58, 0.85] from the analysis of rest polar maps. The identification of dyslipidemia and arterial hypertension showed poor performance and was not statistically significant. CONCLUSION: Multi-task DL for the evaluation of quantitative PET myocardial perfusion polar maps is able to identify an impaired MFR as well as cardiovascular risk traits such as sex, smoking and possibly diabetes at the individual-patient level.

Identification of Hepatic Dendritic Cells in Liver Biopsies Showing Steatosis in Patients with Metabolic Dysfunction-Associated Fatty Liver Disease (MAFLD) Associated with Obesity.

BACKGROUND Metabolic dysfunction-associated fatty liver disease (MAFLD) is now the term used for hepatic steatosis in patients who are overweight or obese, have type 2 diabetes mellitus (T2DM), or evidence of metabolic dysregulation. The prevalence of MAFLD among morbidly obese subjects is 65-93%. Hepatic dendritic cells (hDCs) are antigen-presenting cells that induce T cell-mediated immunity. MAFLD pathogenesis involves numerous immune cell-mediated inflammatory processes, while the particular role of hDCs is yet to be well defined. This study aimed to identify hDCs in liver biopsies from 128 patients with MAFLD associated with obesity. MATERIAL AND METHODS In this cross-sectional study, 128 liver biopsies from 128 patients with MAFLD (diagnosed as presence of hepatic steatosis, plus T2DM, metabolic dysregulation or overweight/obesity) were collected and assessed for CD11c⁺ immunoreactivity degree (CD11c as dendritic cell biomarker), through antigen retrieval, reaction with CD11c antibodies (primary), and marking with diaminobenzidine chromogen. RESULTS Among the 128 patients with MAFLD, 64 (50%) had MAFLD and fibrosis and 72 (56.2%) positively expressed hDCs (CD11c⁺). Among morbidly obese patients, 49 (64.5%) positively expressed hDCs (CD11c⁺) in liver tissue; from patients with obesity grade I- grade II (GI-II), 18 (54.5%) positively expressed hDCs (CD11c⁺) in liver tissue; and from non-obese patients with MAFLD, 5 (26.3%) positively expressed hDCs (CD11c⁺) in liver tissue. CONCLUSIONS hDC expression increases significantly in morbidly obese patients with MAFLD compared with non-obese patients, independent of the degree of fibrosis, suggesting the role of adaptive changes within hDCs in the perpetuation of inflammatory insults in chronic liver diseases.

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies.

PURPOSE OF REVIEW: As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines.

Validation of the European Society of Cardiology pre-test probability model for obstructive coronary artery disease.

AIMS: Estimation of pre-test probability (PTP) of disease in patients with suspected coronary artery disease (CAD) is a common challenge. Due to decreasing prevalence of obstructive CAD in patients referred for diagnostic testing, the European Society of Cardiology suggested a new PTP (2019-ESC-PTP) model. The aim of this study was to validate that model. METHODS AND RESULTS: Symptomatic patients referred for coronary computed tomography angiography (CTA) due to suspected CAD in a geographical uptake area of 3.3 million inhabitants were included. The reference standard was a combined endpoint of CTA and invasive coronary angiography (ICA) with obstructive CAD defined at ICA as a ≥50% diameter stenosis or fractional flow reserve ≤0.80 when performed. The 2019-ESC-PTP, 2013-ESC-PTP, and CAD Consortium basic PTP scores were calculated based on age, sex, and symptoms. Of the 42 328 identified patients, coronary stenosis was detected in 8.8% using the combined endpoint. The 2019-ESC-PTP and CAD Consortium basic scores classified substantially more patients into the low PTP groups (PTP < 15%) than did the 2013-ESC-PTP (64% and 65% vs. 16%, P < 0.001). Using the combined endpoint as reference, calibration of the 2019-ESC-PTP model was superior to the 2013-ESC-PTP and CAD Consortium basic score. CONCLUSION: The new 2019-ESC-PTP model is well calibrated and superior to the previously recommended models in predicting obstructive stenosis detected by a combined endpoint of CTA and ICA.

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT.

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Editorial: International Consensus Recommendations to Replace the Terminology of Non-Alcoholic Fatty Liver Disease (NAFLD) with Metabolic-Associated Fatty Liver Disease (MAFLD).

In 2020, international consensus guidelines recommended the renaming of non-alcoholic fatty liver disease (NAFLD) to metabolic-associated fatty liver disease (MAFLD), supported by diagnostic criteria. MAFLD affects up to 25% of the global population. However, the rates of MAFLD are likely to be underestimated due to the increasing prevalence of type 2 diabetes mellitus (T2DM) and obesity. Within the next decade, MAFLD has been projected to become a major cause of cirrhosis and hepatocellular carcinoma (HCC) worldwide, as well as the most common indication for liver transplantation in the US. This transition in terminology and clinical criteria may increase momentum and clinical evidence at multiple levels, including patient diagnosis, management, and care, and provide the basis for new research areas and clinical development for therapeutics. The diagnostic criteria for MAFLD are practical, simple, and superior to the existing NAFLD criteria for identifying patients at increased risk of developing progressive liver disease. This Editorial aims to present the historical evolution of the terminology for fatty liver disease and the advantages of diagnosis, patient management, and future research on MAFLD.

A Review of the Increasing Prevalence of Metabolic-Associated Fatty Liver Disease (MAFLD) in Children and Adolescents Worldwide and in Mexico and the Implications for Public Health.

Non-alcoholic fatty liver disease (NAFLD) affects almost a quarter of the world's population and is the most common cause of chronic liver disease in children and adolescents. The recent proposal to replace the terminology of NAFLD with metabolic-associated fatty liver disease (MAFLD) aims to reflect the pathophysiology and risk factors for this disease. Importantly, the risk factors for MAFLD may be prenatal, such as genetic factors, or postnatal, such as obesity and insulin resistance. MAFLD is increasingly recognized in children and adolescents. Early diagnosis and identification of high-risk individuals with type 2 diabetes mellitus and metabolic syndrome is important. The diagnosis and management of MAFLD in children and adolescents should follow international clinical guidelines, such as those from the American Diabetes Association (ADA) and the International Society for Pediatric and Adolescent Diabetes (ISPAD). Current guidelines recommend lifestyle and dietary modifications, exercise, screening, individualized patient assessment, and multidisciplinary patient management. This review assesses the revised terminology and discusses the epidemiology, risk factors, pathophysiology, diagnosis, and prevention of MAFLD in children and adolescents worldwide and in Mexico, and also considers the implications for public health.

Prehospital risk stratification in patients with chest pain.

OBJECTIVES: The History, ECG, Age, Risk Factors and Troponin (HEART) Score is a decision support tool applied by physicians in the emergency department developed to risk stratify low-risk patients presenting with chest pain. We assessed the potential value of this tool in prehospital setting, when applied by emergency medical services (EMS), and derived and validated a tool adapted to the prehospital setting in order to determine if it could assist with decisions regarding conveyance to a hospital. METHODS: In 2017, EMS personnel prospectively determined the HEART Score, including point-of-care (POC) troponin measurements, in patients presenting with chest pain, in the north of the Netherlands. The primary endpoint was a major adverse cardiac event (MACE), consisting of acute myocardial infarction or death, within 3 days. The components of the HEART Score were evaluated for their discriminatory value, cut-offs were calibrated for the prehospital setting and sex was substituted for cardiac risk factors to develop a prehospital HEART (preHEART) Score. This score was validated in an independent prospective cohort of 435 patients in 2018. RESULTS: Among 1208 patients prospectively recruited in the first cohort, 123 patients (10.2%) developed a MACE. The HEART Score had a negative predictive value (NPV) of 98.4% (96.4-99.3), a positive predictive value (PPV) of 35.5% (31.8-39.3) and an area under the receiver operating characteristic curve (AUC) of 0.81 (0.78-0.85). The preHEART Score had an NPV of 99.3% (98.1-99.8), a PPV of 49.4% (42.0-56.9) and an AUC of 0.85 (0.82-0.88), outperforming the HEART Score or POC troponin measurements on their own. Similar results were found in a validation cohort. CONCLUSIONS: The HEART Score can be used in the prehospital setting to assist with conveyance decisions and choice of hospitals; however, the preHEART Score outperforms both the HEART Score and single POC troponin measurements when applied by EMS personnel in the prehospital setting.

Many-Body Signatures of Collective Decay in Atomic Chains.

Fully inverted atoms placed at exactly the same location synchronize as they deexcite, and light is emitted in a burst (known as "Dicke's superradiance"). We investigate the role of finite interatomic separation on correlated decay in mesoscopic chains and provide an understanding in terms of collective jump operators. We show that the superradiant burst survives at small distances, despite Hamiltonian dipole-dipole interactions. However, for larger separations, competition between different jump operators leads to dephasing, suppressing superradiance. Collective effects are still significant for arrays with lattice constants of the order of a wavelength, and lead to a photon emission rate that decays nonexponentially in time. We calculate the two-photon correlation function and demonstrate that emission is correlated and directional, as well as sensitive to small changes in the interatomic distance. These features can be measured in current experimental setups, and are robust to realistic imperfections.

Immunosenescence profile and expression of the aging biomarker (p16INK4a) in testicular cancer survivors treated with chemotherapy.

BACKGROUND: Cytotoxic chemotherapy can cure advanced germ cell tumors. Nevertheless, cancer treatment may induce cellular senescence and accelerate molecular aging. The aging process implies an increase of cells expressing p16INK4a and changes in lymphocyte subpopulations. Our aim was to study the potential induction of premature immunosenescence in testicular cancer survivors (TCS) exposed to chemotherapy. METHODS: Case-control exploratory study of TCS treated with chemotherapy (≥3 BEP cycles, disease-free ≥3 months) compared with age matched healthy controls. Peripheral blood mononuclear cells were isolated, and lymphocyte subpopulations were analyzed by flow cytometry. CDKN2A/p16INK4a expression in T cells was measured using qPCR. The percentage of lymphocyte subpopulations and the CDKN2A/p16INK4a expression in TCS were compared with the control group using the Wilcoxon signed-rank test. RESULTS: We included 16 cases and 16 controls. The median age was 27 years (minimum 24, maximum 54) and the median time on surveillance was 26.5 months (minimum 3, maximum192). TCS had a lower percentage of total T cells and CD4+ T cells in total lymphocytes. Among the CD4+ T lymphocytes, TCS had less naïve CD4+ and increased memory CD4+ cells. Within the CD8+ T lymphocytes, TCS exhibited a decrease in the percentage of naïve cells and an increase in CD8 + CD45RA + CD57+ cells. TCS also exhibited decreased memory CD19+ B cells compared to the controls. The relative expression of CDKN2A/p16INK4a in T cells was increased in TCS (mean 1.54; 95% CI of the mean: 1.074-2.005; p = 0.048). CONCLUSION: In this exploratory study, TCS showed increased expression of CDKN2A/p16INK4a and a lymphocyte phenotype that has been associated with immunosenescence. Further studies are warranted to define the clinical implications of these alterations in TCS.

Repeated ketamine administration induces recognition memory impairment together with morphological changes in neurons from ventromedial prefrontal cortex, dorsal striatum, and hippocampus.

Ketamine is an anesthetic agent that antagonizes N-methyl-d-aspartate receptors, inducing psychotic-like symptoms in healthy humans and animals. This agent has been used as a pharmacological tool for studying biochemical and physiological mechanisms underlying the clinical manifestations of schizophrenia. The main goal of this study was to evaluate the effect of repeated injections of ketamine (5 and 10 mg/kg, i.p., daily for 5 days) on recognition memory and neuronal morphology in ICR-CD1 mice. This treatment induced recognition memory impairment in the novel object recognition test and a decrease in dendritic spines density in both dorsal striatum and CA1-hippocampus. Sholl analysis showed that both ketamine doses decrease the dendritic arborization in ventromedial prefrontal cortex, dorsal striatum, and CA1-hippocampus. Finally, dendritic spines morphology was modified by both doses; that is, an increase of the filipodia-type spines (10 mg/kg) and a reduction of the mushroom-type spines (5 and 10 mg/kg) was observed in the ventromedial prefrontal cortex. In the dorsal striatum, the low dose of ketamine induced an increase in long thin spines and a decrease of mushroom spines. Interestingly, in CA1-hippocampus, there was an increase in the mushrooms type spines (5 mg/kg). Current findings suggest that the subchronic blockade of N-methyl-d-aspartate receptor changes the neuronal plasticity of several brain regions putatively related to recognition memory impairment.

Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.

BACKGROUND: A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR). METHODS: 1,234 patients referred to Nitrogen-13 ammonia PET were analyzed. Demographic (4), clinical (8), and functional variables (9) were retrieved and input into a cross-validated ML workflow consisting of feature selection and modeling. Two PET-defined outcome variables were operationalized: (1) any myocardial ischemia (regional MPR < 2.0) and (2) an elevated risk of MACE (global MPR < 2.0). ROC curves were used to evaluate ML performance. RESULTS: 16 features were included for boosted ensemble ML. ML achieved an AUC of 0.72 and 0.71 in identifying patients with myocardial ischemia and with an elevated risk of MACE, respectively. ML performance was superior to logistic regression when the latter used the ESC guidelines risk models variables for both PET-defined labels (P < .001 and P = .01, respectively). CONCLUSIONS: ML is feasible and applicable in the evaluation and utilization of simple and accessible predictors for the identification of patients who will present myocardial ischemia and an elevated risk of MACE in quantitative PET imaging.

Ventricular synchrony is not significantly determined by absolute myocardial perfusion in patients with chronic heart failure: A 13N-ammonia PET study.

BACKGROUND: It is thought that heart failure (HF) patients may benefit from the evaluation of mechanical (dys)synchrony, and an independent inverse relationship between myocardial perfusion and ventricular synchrony has been suggested. We explore the relationship between quantitative myocardial perfusion and synchrony parameters when accounting for the presence and extent of fixed perfusion defects in patients with chronic HF. METHODS: We studied 98 patients with chronic HF who underwent rest and stress Nitrogen-13 ammonia PET. Multivariate analyses of covariance were performed to determine relevant predictors of synchrony (measured as bandwidth, standard deviation, and entropy). RESULTS: In our population, there were 43 (44%) women and 55 men with a mean age of 71 ± 9.6 years. The SRS was the strongest independent predictor of mechanical synchrony variables (p < .01), among other considered predictors including: age, sex, body mass index, smoking, diabetes mellitus, dyslipidemia, hypertension, rest myocardial blood flow (MBF), and myocardial perfusion reserve (MPR). Results were similar when considering stress MBF instead of MPR. CONCLUSIONS: The existence and extent of fixed perfusion defects, but not the quantitative PET myocardial perfusion parameters (sMBF and MPR), constitute a significant independent predictor of ventricular mechanical synchrony in patients with chronic HF.

Software reproducibility of myocardial blood flow and flow reserve quantification in ischemic heart disease: A 13N-ammonia PET study.

BACKGROUND: We explored agreement in the quantification of myocardial perfusion by cross-comparison of implemented software packages (SPs) in three distinguishable patient profile populations. METHODS: We studied 91 scans of patients divided into 3 subgroups based on their semi-quantitative perfusion findings: patients with normal perfusion, with reversible perfusion defects, and with fixed perfusion defects. Rest myocardial blood flow (MBF), stress MBF, and myocardial flow reserve (MFR) were obtained with QPET, SyngoMBF, and Carimas. Agreement between SPs was considered adequate when a pairwise standardized difference was found to be < 0.20 and its corresponding intraclass correlation coefficient was ≥ 0.75. RESULTS: In patients with normal perfusion, two out of three comparisons of global stress MBF quantifications were outside the limits of agreement. In ischemic patients, all comparisons of global stress MBF and MFR were outside the limits of established agreement. In patients with fixed perfusion defects, all SP comparisons of perfusion quantifications were within the limit of agreement. Regionally, agreement of these perfusion estimates was mostly found for the left anterior descending artery vascular territory. CONCLUSION: Reversible defects demonstrated the worst agreement in global stress MBF and MFR and discrepancies showed to be regional dependent. Reproducibility between SPs should not be assumed.

Whipple disease diagnosed by enteroscopy: first case report in Colombia of an underdiagnosed disease and literature review.

BACKGROUND: Whipple's disease is a rare systemic disease caused by a gram-positive bacillus called Tropheryma whipplei. First described in 1907 as an intestinal lipodystrophy with histological finding of vacuoles in the macrophages of the intestinal mucous. Usually the symptoms are localized according to the compromised organ. The differential diagnosis is wide. It can be fatal without proper treatment. Recurrence can occur in up to 33% of the cases and usually compromises the neurological system. CASE PRESENTATION: This article reports the case of a 46-year-old female patient with a history of a 6-month hypochromic microcytic anemia of unknown cause. She consulted for a 6-months oppressive abdominal pain located in the mesogastrium as well as abdominal distention associated with nausea and liquid stools; in addition, she had an 8-month small and medium joint pain, without edema or erythema. Physical examination without relevant findings. Multiple esophagogastroduodenoscopies with normal gastric and duodenal biopsies findings and a normal colonoscopy were performed. Endoscope capsule showed red spots in the duodenum and ulcerations in the jejunum and proximal ileum covered by fibrin; histological report showed macrophages with positive periodic acid-schiff reaction staining (PAS staining), disgnosing Whipple's disease. Antibiotics were initiated. The patient is currently in the second phase of treatment without gastrointestinal and joint symptoms. CONCLUSION: This is the first case reported in Colombia. It is a rare entity and difficult to diagnose reason why it is important to continue with clinical investigations to give more clarity about the onset and appropriate diagnose to avoid the delay in treatment of this entity.

Change in abdominal, but not femoral subcutaneous fat CT-radiodensity is associated with improved metabolic profile after bariatric surgery.

BACKGROUND AND AIMS: Computed tomography (CT)-derived adipose tissue radiodensity represents a potential noninvasive surrogate marker for lipid deposition and obesity-related metabolic disease risk. We studied the effects of bariatric surgery on CT-derived adipose radiodensities in abdominal and femoral areas and their relationships to circulating metabolites in morbidly obese patients. METHODS AND RESULTS: We examined 23 morbidly obese women who underwent CT imaging before and 6 months after bariatric surgery. Fifteen healthy non-obese women served as controls. Radiodensities of the abdominal subcutaneous (SAT) and visceral adipose tissue (VAT), and the femoral SAT, adipose tissue masses were measured in all participants. Circulating metabolites were measured by NMR. At baseline, radiodensities of abdominal fat depots were lower in the obese patients as compared to the controls. Surprisingly, radiodensity of femoral SAT was higher in the obese as compared to the controls. In the abdominal SAT depot, radiodensity strongly correlated with SAT mass (r = -0.72, p < 0.001). After surgery, the radiodensities of abdominal fat increased significantly (both p < 0.01), while femoral SAT radiodensity remained unchanged. Circulating ApoB/ApoA-I, leucine, valine, and GlycA decreased, while glycine levels significantly increased as compared to pre-surgical values (all p < 0.05). The increase in abdominal fat radiodensity correlated negatively with the decreased levels of ApoB/ApoA-I ratio, leucine and GlycA (all p < 0.05). The increase in abdominal SAT density was significantly correlated with the decrease in the fat depot mass (r = -0.66, p = 0.002). CONCLUSION: Higher lipid content in abdominal fat depots, and lower content in femoral subcutaneous fat, constitute prominent pathophysiological features in morbid obesity. Further studies are needed to clarify the role of non-abdominal subcutaneous fat in the pathogenesis of obesity. CLINICAL TRIAL REGISTRATION NUMBER: NCT01373892.