Kazem Behbehani

Bio: Kazem Behbehani is an academic researcher. The author has contributed to research in topics: Population & Insulin resistance. The author has an hindex of 26, co-authored 54 publications receiving 1707 citations.

Obesity Is a Positive Modulator of IL-6R and IL-6 Expression in the Subcutaneous Adipose Tissue: Significance for Metabolic Inflammation

Abstract: The role of IL-6R/IL-6 axis in metabolic inflammation remains controversial. We determined the changes in adipose tissue expression of IL-6R and IL-6 in obese, overweight, and lean non-diabetic individuals. Subcutaneous adipose tissue biopsies were collected from 33 obese, 22 overweight, and 10 lean individuals and the expression of IL-6R, IL-6, TNF-α, MCP-1, IP-10, CD11b, CD163, and CD68 was detected by immunohistochemistry; results were also confirmed by real-time RT-PCR and confocal microscopy. The data were compared using unpaired t-test and the dependence between two variables was assessed by Pearson’s correlation test. Obese individuals showed higher IL-6R expression (103.8±4.807) in the adipose tissue as compared with lean/overweight (68.06±4.179) subjects (P<0.0001). The elevated IL-6R expression correlated positively with body mass index (BMI) (r=0.80 P<0.0001) and percent body fat (r=0.69 P=0.003). The increased IL-6R expression in obesity was also confirmed by RT-PCR (Obese: 3.921±0.712 fold; Lean/Overweight: 2.191±0.445 fold; P=0.0453) and confocal microscopy. IL-6 expression was also enhanced in obese adipose tissue (127.0±15.91) as compared with lean/overweight (86.69±5.25) individuals (P=0.03) which correlated positively with BMI (r=0.58 P=0.008). IL-6 mRNA expression was concordantly higher in obese (16.60±2.214 fold) versus lean/overweight (9.376±1.656 fold) individuals (P=0.0108). These changes in the IL-6R/IL-6 expression correlated positively with the adipose tissue expression of CD11b (IL-6R r=0.44 P=0.063; IL-6 r=0.77 P<0.0001), CD163 (IL-6R r=0.45 P=0.045; IL-6 r=0.55 P=0.013), TNF-α (IL-6R r=0.73 P=0.0003; IL-6 r=0.60 P=0.008), MCP-1 (IL-6R r=0.61 P=0.005; IL-6 r=0.63 P=0.004) and IP-10 (IL-6R r=0.41 P=0.08; IL-6 r=0.50 P=0.026). It was, therefore, concluded that obesity was a positive modulator of IL-6R and IL-6 expression in the adipose tissue which might be a contributory mechanism to induce metabolic inflammation.

Higher plasma betatrophin/ANGPTL8 level in Type 2 Diabetes subjects does not correlate with blood glucose or insulin resistance

Abstract: Betatrophin/ANGPTL8 is a newly identified hormone produced in liver and adipose tissue that has been shown to be induced as a result of insulin resistance and regulates lipid metabolism. Little is known about betatrophin level in humans and its association with T2D and metabolic risk factors. Plasma level of betatrophin was measured by ELISA in 1603 subjects: 1047 non-diabetic and 556 T2D subjects and its associations with metabolic risk factors in both non-diabetic and T2D were also studied. Our data show a significant difference in betatrophin levels between non-diabetic (731.3 (59.5–10625.0) pg/ml) and T2D (1710.5 (197.4–12361.1) p < 0.001. Betatrophin was positively correlated with age, BMI, waist/hip ratio, FBG, HbA1C, HOMA-IR and TG in the non-diabetic subjects. However, no association was observed with BMI, FBG, HbA1C or HOMA-IR in T2D subjects. TC and LDL showed negative association with betatrophin in T2D subjects. Multivariate analysis showed that subjects in the highest tertile of betatrophin had higher odds of having T2D (odd ratio [OR] = 6.15, 95% confidence interval [CI] = (3.15 – 12.01). Our data show strong positive associations between betatrophin and FBG and insulin resistance in non-diabetic subjects. However, correlations with FBG and insulin resistance were diminished in T2D subjects.

Metabolic Disease Risk in Children by Salivary Biomarker Analysis

Abstract: Objective: The study of obesity-related metabolic syndrome or Type 2 diabetes (T2D) in children is particularly difficult because of fear of needles. We tested a non-invasive approach to study inflammatory parameters in an at-risk population of children to provide proof-of-principle for future investigations of vulnerable subjects. Design and Methods: We evaluated metabolic differences in 744, 11-year old children selected from underweight, normal healthy weight, overweight and obese categories by analyzing fasting saliva samples for 20 biomarkers. Saliva supernatants were obtained following centrifugation and used for analyses. Results: Salivary C-reactive protein (CRP) was 6 times higher, salivary insulin and leptin were 3 times higher, and adiponectin was 30% lower in obese children compared to healthy normal weight children (all P,0.0001). Categorical analysis suggested that there might be three types of obesity in children. Distinctly inflammatory characteristics appeared in 76% of obese children while in 13%, salivary insulin was high but not associated with inflammatory mediators. The remaining 11% of obese children had high insulin and reduced adiponectin. Forty percent of the non-obese children were found in groups which, based on biomarker characteristics, may be at risk for becoming obese. Conclusions: Significantly altered levels of salivary biomarkers in obese children from a high-risk population, suggest the potential for developing non-invasive screening procedures to identify T2D-vulnerable individuals and a means to test preventative strategies.

Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait-a cohort study

Abstract: Objective: We build classification models and risk assessment tools for diabetes, hypertension and comorbidity using machine-learning algorithms on data from Kuwait. We model the increased proneness in diabetic patients to develop hypertension and vice versa. We ascertain the importance of ethnicity (and natives vs expatriate migrants) and of using regional data in risk assessment. Design: Retrospective cohort study. Four machinelearning techniques were used: logistic regression, k-nearest neighbours (k-NN), multifactor dimensionality reduction and support vector machines. The study uses fivefold cross validation to obtain generalisation accuracies and errors. Setting: Kuwait Health Network (KHN) that integrates data from primary health centres and hospitals in Kuwait. Participants: 270 172 hospital visitors (of which, 89 858 are diabetic, 58 745 hypertensive and 30 522 comorbid) comprising Kuwaiti natives, Asian and Arab expatriates. Outcome measures: Incident type 2 diabetes, hypertension and comorbidity. Results: Classification accuracies of >85% (for diabetes) and >90% (for hypertension) are achieved using only simple non-laboratory-based parameters. Risk assessment tools based on k-NN classification models are able to assign ‘high’ risk to 75% of diabetic patients and to 94% of hypertensive patients. Only 5% of diabetic patients are seen assigned ‘low’ risk. Asian-specific models and assessments perform even better. Pathological conditions of diabetes in the general population or in hypertensive population and those of hypertension are modelled. Two-stage aggregate classification models and risk assessment tools, built combining both the component models on diabetes (or on hypertension), perform better than individual models. Conclusions: Data on diabetes, hypertension and comorbidity from the cosmopolitan State of Kuwait are available for the first time. This enabled us to apply four different case–control models to assess risks. These tools aid in the preliminary non-intrusive assessment of

Proteomics Analysis of Human Obesity Reveals the Epigenetic Factor HDAC4 as a Potential Target for Obesity

Abstract: Sedentary lifestyle and excessive energy intake are prominent contributors to obesity; a major risk factors for the development of insulin resistance, type 2 diabetes and cardiovascular diseases. Elucidating the molecular mechanisms underlying these chronic conditions is of relevant importance as it might lead to the identification of novel anti-obesity targets. The purpose of the current study is to investigate differentially expressed proteins between lean and obese subjects through a shot-gun quantitative proteomics approach using peripheral blood mononuclear cells (PBMCs) extracts as well as potential modulation of those proteins by physical exercise. Using this approach, a total of 47 proteins showed at least 1.5 fold change between lean and obese subjects. In obese, the proteomic profiling before and after 3 months of physical exercise showed differential expression of 38 proteins. Thrombospondin 1 (TSP1) was among the proteins that were upregulated in obese subjects and then decreased by physical exercise. Conversely, the histone deacetylase 4 (HDAC4) was downregulated in obese subjects and then induced by physical exercise. The proteomic data was further validated by qRT-PCR, Western blot and immunohistochemistry in both PBMCs and adipose tissue. We also showed that HDAC4 levels correlated positively with maximum oxygen consumption (VO2 Max) but negatively with body mass index, percent body fat, and the inflammatory chemokine RANTES. In functional assays, our data indicated that ectopic expression of HDAC4 significantly impaired TNF-α-dependent activation of NF-κB, establishing thus a link between HDAC4 and regulation of the immune system. Together, the expression pattern of HDAC4 in obese subjects before and after physical exercise, its correlation with various physical, clinical and metabolic parameters along with its inhibitory effect on NF-κB are suggestive of a protective role of HDAC4 against obesity. HDAC4 could therefore represent a potential therapeutic target for the control and management of obesity and presumably insulin resistance.

Listeria monocytogenes, a food-borne pathogen

Abstract: Listeria monocytogenes is a Gram positive, aerobic, facultative anaerobic and nonacid fast bacterium, which can cause the disease listeriosis in both human and animals. It is widely distributed thoroughout the environment and has been isolated from various plant and animal food products associated with listeriosis outbreaks. Contaminated ready-to-eat food products such as gravad and cold-smoked salmon and rainbow trout have been associated with human listeriosis in Sweden. The aim of this study was to analyse the occurrence and level of L. monocytogenes in gravad and cold-smoked salmon (Salmo salar) products packed under vacuum or modified atmosphere from retail outlets in Sweden. Isolated strains were characterized by serotyping and the diversity of the strains within and between producers were determined with PFGE (Pulsed-field gel electrophoresis). The characterized fish isolates were compared with previously characterized human strains. L. monocytogenes was isolated from 11 (three manufacturers) of 56 products analysed. This included gravad salmon products from three manufacturers and cold-smoked salmon from one manufacturer. The highest level of L. monocytogenes found was 1500 cfu/g from a cold-smoked salmon product but the level was low (<100 cfu/g) in most of the products. Serovar 1/2a was predominant, followed by 4b. Three products of gravad salmon harboured more than one serovar. PFGE typing of the 56 salmon isolates detected five Asc I types: four types were identical to human clinical strains with Asc I and one was identical and one was closely related to human clinical strains with Apa I. Isolation of identical or closely related L. monocytogenes strains from human clinical cases of listeriosis and gravad and cold-smoked salmon suggested that these kinds of products are possible sources of listeriosis in Sweden. Therefore, these products should be considered risk products for human listeriosis.

Machine Learning and Data Mining Methods in Diabetes Research.

Abstract: The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Interleukin-33 in health and disease

Abstract: Interleukin-33 (IL-33) - a member of the IL-1 family - was originally described as an inducer of type 2 immune responses, activating T helper 2 (TH2) cells and mast cells. Now, evidence is accumulating that IL-33 also potently stimulates group 2 innate lymphoid cells (ILC2s), regulatory T (Treg) cells, TH1 cells, CD8+ T cells and natural killer (NK) cells. This pleiotropic nature is reflected in the role of IL-33 in tissue and metabolic homeostasis, infection, inflammation, cancer and diseases of the central nervous system. In this Review, we highlight the molecular and cellular characteristics of IL-33, together with its major role in health and disease and the potential therapeutic implications of these findings in humans.

The genetic architecture of type 2 diabetes

Abstract: The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.