Avshalom Leibowitz

Dr. Avshalom Leibowitz


Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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23 August 2018 in Nutrients
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Journal Article
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ABAriel Bier
Ariel Bier
TBTzipi Braun
Tzipi Braun
RKRawan Khasbab
Rawan Khasbab
ASAyelet Di Segni
Ayelet Di Segni
Ehud GrossmanEhud Grossman
Ehud Grossman
Avshalom LeibowitzAvshalom Leibowitz
Avshalom Leibowitz
Emerging data indicate a correlation between gut microbial composition and cardiovascular disease including hypertension. The host’s diet greatly affects microbial composition and metabolite production. Short chain fatty acids (SCFAs) are products of microbial fermentation, which can be utilized by the host. It has been suggested that SCFAs play a pivotal role as mediators in a microbiome host: microbial interactions occur in health and disease. The aim of this study was to evaluate the effect of a high salt diet (HSD) on microbial variation and to determine whether this effect is accompanied by an alteration in fecal SCFAs. To this end, Dahl salt-sensitive rats were divided into two groups (n = 10 each): (A) Control: fed regular chow; and (B) Fed HSD. High-throughput pyrosequencing of the 16S rRNA amplicon sequencing was used for microbiome characterizing. Chromatography-mass spectrometry was used to measure the levels of SCFAs: acetic acid, propionic acid, butyric acid, and isobutyric acid in fecal samples. Differences in microbial composition were noted between groups. Principal Coordinate Analysis (PCoA) principal coordinate 1 (PC1) primarily separated controls from the HSD. Four taxa displayed significant differences between HSD and controls. Taxa from the Erwinia genus, the Christensenellaceae and Corynebacteriaceae families, displayed an increased abundance in HSD versus control. In contrast, taxa from the Anaerostipes genus displayed a decreased abundance in HSD. We were able to identify seven unique taxa that were significantly associated with blood pressure. There was a significant difference in fecal acetic acid, as well as propionic and isobutyric acid, but not in the butyric acid composition between groups. Adding salt to a diet impacts the gut’s microbial composition, which may alter fecal SCFA production.

27 April 2019 in Cardiovascular Diabetology
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Journal Article
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MGMika Geva
Mika Geva
GSGadi Shlomai
Gadi Shlomai
ABAnat Berkovitch
Anat Berkovitch
EMElad Maor
Elad Maor
Avshalom LeibowitzAvshalom Leibowitz
Avshalom Leibowitz
Ehud GrossmanEhud Grossman
Ehud Grossman
Prediabetes is a well-established risk factor for progression to overt diabetes mellitus (DM), which is in turn associated with development of hypertension (HTN) and vice versa. However, the role of prediabetes and HbA1c in particular as an independent risk factor for the development of hypertension is unclear. In this current study, we aimed to evaluate the association between both fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) levels in the prediabetes range and development of HTN among a large cohort of normotensive subjects. We investigated 5016 normotensive participants without DM and other cardiovascular risk factors who were annually screened in a tertiary medical center. Subjects were divided into normoglycemic and prediabetic groups. Normoglycemia was defined as HbA1c < 5.7% and FPG < 100 mg/dl. Prediabetes was defined according to the ADA criteria, i.e., 6.5% > HbA1c ≥ 5.7% or impaired fasting glucose (IFG):126 mg/dl > FPG ≥ 100 mg/dl. Subgroup analysis was made by dividing participants into four groups according to FPG and HbA1C levels, i.e., normoglycemia, impaired HbA1c only, IFG only, and both parameters impaired. During a follow-up of 3.7 ± 2.9 years, 318 (6.3%) subjects developed HTN. A cumulative hazard function for the development of hypertension showed a 2.89-fold ([95% CI 2.19–3.83], p < .0001) increased risk for HTN in the prediabetic population. In a multivariable Cox proportional hazard regression model adjusted to common confounding risk factors for HTN, prediabetes was found to be independently associated with a 1.95-fold ([95%, CI 1.43–2.52] p < .0001) increased risk for hypertension. Impaired HbA1C only was not found to be independently associated with HTN, while IFG only showed a 2.13-fold (95%, [CI 1.46–3.11] p < .0001) increased risk for HTN compared to normoglycemic, and a 2.55-fold ([95% CI 1.85–3.51] p < .0001) increased risk for HTN when both parameters impaired. Our study demonstrates that FPG in the prediabetes range, albeit not glycated hemoglobin, is independently and significantly associated with future development of HTN. Therefore, our findings further highlight the pivotal predictive role of IFG for HTN development as opposed to the limited independent role of abnormal HbA1c levels.

18 November 2015 in Journal of Pineal Research
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Avshalom LeibowitzAvshalom Leibowitz
Avshalom Leibowitz
AVAlexander Volkov
Alexander Volkov
KVKonstantin Voloshin
Konstantin Voloshin
CSChen Shemesh
Chen Shemesh
IBIris Barshack
Iris Barshack
EGEhud Grossman
Ehud Grossman

9 June 2022 in JMIR Formative Research
Verified
Journal Article
Open access
EIEdward Itelman
Edward Itelman
GSGadi Shlomai
Gadi Shlomai
Avshalom LeibowitzAvshalom Leibowitz
Avshalom Leibowitz
SWSHIRI WEINSTEIN
SHIRI WEINSTEIN
MYMaya Yakir
Maya Yakir
GSGad Segal
Gad Segal
Background: Patients admitted to general wards are inherently at risk of deterioration. Thus, tools that can provide early detection of deterioration may be lifesaving. Frequent remote patient monitoring (RPM) has the potential to allow such early detection, leading to a timely intervention by health care providers. Objective: This study aimed to assess the potential of a novel wearable RPM device to provide timely alerts in patients at high risk for deterioration. Methods: This prospective observational study was conducted in two general wards of a large tertiary medical center. Patients determined to be at high risk to deteriorate upon admission and assigned to a telemetry bed were included. On top of the standard monitoring equipment, a wearable monitor was attached to each patient, and monitoring was conducted in parallel. The data gathered by the wearable monitors were analyzed retrospectively, with the medical staff being blinded to them in real time. Several early warning scores of the risk for deterioration were used, all calculated from frequent data collected by the wearable RPM device: these included (1) the National Early Warning Score (NEWS), (2) Airway, Breathing, Circulation, Neurology, and Other (ABCNO) score, and (3) deterioration criteria defined by the clinical team as a “wish list” score. In all three systems, the risk scores were calculated every 5 minutes using the data frequently collected by the wearable RPM device. Data generated by the early warning scores were compared with those obtained from the clinical records of actual deterioration among these patients. Results: In total, 410 patients were recruited and 217 were included in the final analysis. The median age was 71 (IQR 62-78) years and 130 (59.9%) of them were male. Actual clinical deterioration occurred in 24 patients. The NEWS indicated high alert in 16 of these 24 (67%) patients, preceding actual clinical deterioration by 29 hours on average. The ABCNO score indicated high alert in 18 (75%) of these patients, preceding actual clinical deterioration by 38 hours on average. Early warning based on wish list scoring criteria was observed for all 24 patients 40 hours on average before clinical deterioration was detected by the medical staff. Importantly, early warning based on the wish list scoring criteria was also observed among all other patients who did not deteriorate. Conclusions: Frequent remote patient monitoring has the potential for early detection of a high risk to deteriorate among hospitalized patients, using both grouped signal-based scores and algorithm-based prediction. In this study, we show the ability to formulate scores for early warning by using RPM. Nevertheless, early warning scores compiled on the basis of these data failed to deliver reasonable specificity. Further efforts should be directed at improving the specificity and sensitivity of such tools. Trial Registration: ClinicalTrials.gov NCT04220359; https://clinicaltrials.gov/ct2/show/NCT04220359

1 November 2016 in European Journal of Internal Medicine
Verified
Journal Article
Closed access
ISInbal Shafran
Inbal Shafran
GGGahl Greenberg
Gahl Greenberg
EGEhud Grossman
Ehud Grossman
Avshalom LeibowitzAvshalom Leibowitz
Avshalom Leibowitz

3 May 2024 in Nutrients
Verified
Journal Article
Open access
PDF file
HRHadar Raphael
Hadar Raphael
EKEyal Klang
Eyal Klang
EKEli Konen
Eli Konen
YIYael Inbar
Yael Inbar
Avshalom LeibowitzAvshalom Leibowitz
Avshalom Leibowitz
Ehud GrossmanEhud Grossman
Ehud Grossman
Background: Obesity is associated with metabolic syndrome and fat accumulation in various organs such as the liver and the kidneys. Our goal was to assess, using magnetic resonance imaging (MRI) Dual-Echo phase sequencing, the association between liver and kidney fat deposition and their relation to obesity. Methods: We analyzed MRI scans of individuals who were referred to the Chaim Sheba Medical Center between December 2017 and May 2020 to perform a study for any indication. For each individual, we retrieved from the computerized charts data on sex, and age, weight, height, body mass index (BMI), systolic and diastolic blood pressure (BP), and comorbidities (diabetes mellitus, hypertension, dyslipidemia). Results: We screened MRI studies of 399 subjects with a median age of 51 years, 52.4% of whom were women, and a median BMI 24.6 kg/m2. We diagnosed 18% of the participants with fatty liver and 18.6% with fat accumulation in the kidneys (fatty kidneys). Out of the 67 patients with fatty livers, 23 (34.3%) also had fatty kidneys, whereas among the 315 patients without fatty livers, only 48 patients (15.2%) had fatty kidneys (p < 0.01). In comparison to the patients who did not have a fatty liver or fatty kidneys (n = 267), those who had both (n = 23) were more obese, had higher systolic BP, and were more likely to have diabetes mellitus. In comparison to the patients without a fatty liver, those with fatty livers had an adjusted odds ratio of 2.91 (97.5% CI; 1.61–5.25) to have fatty kidneys. In total, 19.6% of the individuals were obese (BMI ≥ 30), and 26.1% had overweight (25 < BMI < 30). The obese and overweight individuals were older and more likely to have diabetes mellitus and hypertension and had higher rates of fatty livers and fatty kidneys. Fat deposition in both the liver and the kidneys was observed in 15.9% of the obese patients, in 8.3% of the overweight patients, and in none of those with normal weight. Obesity was the only risk factor for fatty kidneys and fatty livers, with an adjusted OR of 6.3 (97.5% CI 2.1–18.6). Conclusions: Obesity is a major risk factor for developing a fatty liver and fatty kidneys. Individuals with a fatty liver are more likely to have fatty kidneys. MRI is an accurate modality for diagnosing fatty kidneys. Reviewing MRI scans of any indication should include assessment of fat fractions in the kidneys in addition to that of the liver.