Risk factors for type 2 diabetes mellitus and cardiovascular diseases in Hispanic adolescents

This cross-sectional study evaluated risk factors (RF) for type 2 diabetes (T2DM) and cardiovascular diseases (CVD) in 100 Hispanic adolescents(50 overweight, 50 non-overweight) aged 12-16 years, and their associations with body mass index (BMI), diet, physical activity (PA), gender, and birth weight (BW). The RF studied were fasting plasma glucose (FPG), insulin sensitivity (IS), total cholesterol (TC), triacylglycerols (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), acanthosis nigricans (AN), and blood pressure (BP). Dietary intakes were assessed using the Block Kids Questionnaire, fat-related intake behavior (FB) using the Fat-Related Diet Habits Questionnaire, and PA using the Modifiable Activity Questionnaire for Adolescents. Blood was collected after an overnight fast of 12 hours. All statistical analyses used SPSS 14.0. Overweight adolescents had presence of AN, higher BP, TC, TG, and LDL, and lower IS, ps < .001, as compared to non-overweight adolescents. Overweight adolescents were more likely to have 1 and 2 RF for T2DM and CVD as compared to having 0, ps < .001, and 2 RF as compared to having 1, p =.033. Adolescents with kilocalorie (Kcal) intake above requirements for age gender, and PA level were 4.6 times more likely to be overweight, p = .005. Overweight adolescents had worse FB, p = .011, and lower PA, p < .001. Adolescents with worse FB had higher BP, p = .016. Fiber below recommendations (14g/1,000 Kcal) was associated with being overweight, p = .012, and lower IS, p = .040. Adolescents with higher BW had higher FPG, p = .013. Our findings point to an association between being overweight and RF for T2DM and CVD, suggesting that overweight during adolescence may have serious health consequences for Hispanic adolescents. Also, our results indicate that Hispanic overweight adolescents eat more Kcal and less fiber than required, have worst FB, and less PA levels than their non-overweight counterparts. In addition, high BW and dietary habits of Hispanic adolescents, such as low fiber and FB, increase their risk for T2DM and CVD. We conclude that BMI can serve as a useful tool to identify Hispanic adolescents at risk for T2DM and CVD.

Automated detection of hematological abnormalities through classification of flow cytometric data patterns

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification ability than is currently available. Toward this end, the purpose of this dissertation was to establish a feature extraction and pattern classification framework capable of providing improved accuracy for detecting specific hematological abnormalities in flow cytometric blood data. ^ This involved extracting a unique and powerful set of shift-invariant statistical features from the multi-dimensional flow cytometry data and then using these features as inputs to a pattern classification engine composed of an artificial neural network (ANN). The contribution of this method consisted of developing a descriptor matrix that can be used to reliably assess if a donor’s blood pattern exhibits a clinically abnormal level of variant lymphocytes, which are blood cells that are potentially indicative of disorders such as leukemia and infectious mononucleosis. ^ This study showed that the set of shift-and-rotation-invariant statistical features extracted from the eigensystem of the flow cytometric data pattern performs better than other commonly-used features in this type of disease detection, exhibiting an accuracy of 80.7%, a sensitivity of 72.3%, and a specificity of 89.2%. This performance represents a major improvement for this type of hematological classifier, which has historically been plagued by poor performance, with accuracies as low as 60% in some cases. This research ultimately shows that an improved feature space was developed that can deliver improved performance for the detection of variant lymphocytes in human blood, thus providing significant utility in the realm of suspect flagging algorithms for the detection of blood-related diseases.^

Synthesis of azulenylsilane nitrones as diagnostic tools for superoxide detection

The superoxide radical is considered to play important roles in physiological processes as well as in the genesis of diverse cytotoxic conditions such as cancer, various cardiovascular disorders and neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD) and Alzheimer’s disease (AD). The detection and quantification of superoxide within cells is of critical importance to understand biological roles of superoxide and to develop preventive strategies against free radical-mediated diseases. Cyclic nitrone spin traps such as DMPO, EMPO, DEPMPO, BMPO and their derivatives have been widely used in conjunction with ESR spectroscopy to detect cellular superoxide with some success. However, the formation of unstable superoxide adducts from the reaction of cyclic nitrones with superoxide is a stumbling block in detecting superoxide by using electron spin resonance (ESR). A chemiluminescent probe, lucigenin, and fluorogenic probes, hydroethidium and MitoSox, are the other frequently used methods in detecting superoxide. However, luceginen undergoes redox-cycling producing superoxide by itself, and hydroethidium and MitoSox react with other oxidants apart from superoxide forming red fluorescent products contributing to artefacts in these assays. Hence, both methods were deemed to be inappropriate for superoxide detection. In this study, an effective approach, a selective mechanism-based colorimetric detection of superoxide anion has been developed by using silylated azulenyl nitrones spin traps. Since a nitrone moiety and an adjacent silyl group react readily with radicals and oxygen anions respectively, such nitrones can trap superoxide efficiently because superoxide is both a radical and an oxygen anion. Moreover, the synthesized nitrone is designed to be triggered solely by superoxide and not by other commonly observed oxygen radicals such as hydroxyl radical, alkoxyl radicals and peroxyl radical. In vitro studies have shown that these synthesized silylated azylenyl nitrones and the mitochondrial-targeted guanylhydrazone analog can trap superoxide efficiently yielding UV-vis identifiable and even potentially fluorescence-detectable orange products. Therefore, the chromotropic detection of superoxide using these nitrones can be a promising method in contrast to other available methods.

Localized surface plasmon resonance biosensors for real-time biomolecular binding study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ∼7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.

Healthy Eating Index scores associated with symptoms of depression in Cuban-Americans with and without type 2 diabetes: a cross sectional study

Background Low diet quality and depression symptoms are independently associated with poor glycemic control in subjects with type 2 diabetes (T2D); however, the relationship between them is unclear. The aim of this study was to determine the association between diet quality and symptoms of depression among Cuban-Americans with and without T2D living in South Florida. Methods Subjects (n = 356) were recruited from randomly selected mailing list. Diet quality was determined using the Healthy Eating Index-2005 (HEI-05) score. Symptoms of depression were assessed using the Beck Depression Inventory (BDI). Both linear and logistic regression analyses were run to determine whether or not these two variables were related. Symptoms of depression was the dependent variable and independent variables included HEI-05, gender, age, marital status, BMI, education level, A1C, employment status, depression medication, duration of diabetes, and diabetes status. Analysis of covariance was used to test for interactions among variables. Results An interaction between diabetes status, gender and HEI-05 was found (P = 0.011). Among males with a HEI-05 score ≤ 55.6, those with T2D had a higher mean BDI score than those without T2D (11.6 vs. 6.6 respectively, P = 0.028). Among males and females with a HEI-05 score ≤ 55.6, females without T2D had a higher mean BDI score compared to males without T2D (11.0 vs. 6.6 respectively, P = 0.012) Conclusions Differences in symptoms of depression according to diabetes status and gender are found in Cuban-Americans with low diet quality.

Healthy Eating Index and Alternate Healthy Eating Index among Haitian Americans and African Americans with and without Type 2 Diabetes

Ethnicities within Black populations have not been distinguished in most nutrition studies. We sought to examine dietary differences between African Americans (AA) and Haitian Americans (HA) with and without type 2 diabetes using the Healthy Eating Index, 2005 (HEI-05), and the Alternate Healthy Eating Index (AHEI). The design was cross-sectional (225 AA, 246 HA) and recruitment was by community outreach. The eating indices were calculated from data collected with the Harvard food-frequency questionnaire. African Americans had lower HEI-05 scores (−8.67, 13.1); , than HA. Haitian American females and AA males had higher AHEI than AA females and HA males, respectively, () adjusting for age and education. Participants with diabetes had higher adherence to the HEI-05 (1.78, 6.01), , and lower adherence to the AHEI (16.3, −3.19), , , than participants without diabetes. The findings underscore the importance of disaggregating ethnicities and disease state when assessing diet.

Obesity Indicators by Race/Ethnicity for Diagnosis of Cardiometabolic Diseases for a US Representative Sample of Adults

Background: Obesity, a growing epidemic, is a preventable risk factor for cardiometabolic diseases. Obesity and cardiometabolic diseases affect Hispanics and African Americans more than non-Hispanic Caucasians. This study examined the relationship among race/ethnicity, obesity diagnostic measures (body mass index, waist circumference, subscapular and triceps skinfold thickness), and cardiometabolic risk factors (hyperglycemia, high, non-high-density lipoprotein cholesterol, low, high-density lipoprotein cholesterol, and hypertension) for adults across the United States. Methods: Using data from two-cycles of the National Health and Examination Survey (NHANES) 2007-2010, and accounting for the complex sample design, logistic regression models were conducted comparing obesity indicators in Mexican Americans, other Hispanics, and Black non-Hispanics, with White non-Hispanics and their associations with the presence of cardiometabolic diseases. Results: Differences by race/ethnicity were found for subscapular skinfold thickness and hyperglycemia. Waist circumference and subscapular skinfold were positively associated with the presence of hyperglycemia; dyslipidemia, and hypertension across race/ ethnicity, adjusting for age, gender, smoking, physical activity, education, income to poverty index, and health insurance. Race/ ethnicity did not influence the association of any obesity indicators with the tested cardiometabolic diseases. All obesity measures except triceps skinfold were associated with hyperglycemia. Conclusions: We suggest that subscapular skinfold thickness be considered as an inexpensive non-intrusive screening tool for cardiometabolic risk factors in an adult US population

Localized Surface Plasmon Resonance Biosensors for Real-Time Biomolecular Binding Study

Surface Plasmon Resonance (SPR) and localized surface plasmon resonance (LSPR) biosensors have brought a revolutionary change to in vitro study of biological and biochemical processes due to its ability to measure extremely small changes in surface refractive index (RI), binding equilibrium and kinetics. Strategies based on LSPR have been employed to enhance the sensitivity for a variety of applications, such as diagnosis of diseases, environmental analysis, food safety, and chemical threat detection. In LSPR spectroscopy, absorption and scattering of light are greatly enhanced at frequencies that excite the LSPR, resulting in a characteristic extinction spectrum that depends on the RI of the surrounding medium. Compositional and conformational change within the surrounding medium near the sensing surface could therefore be detected as shifts in the extinction spectrum. This dissertation specifically focuses on the development and evaluation of highly sensitive LSPR biosensors for in situ study of biomolecular binding process by incorporating nanotechnology. Compared to traditional methods for biomolecular binding studies, LSPR-based biosensors offer real-time, label free detection. First, we modified the gold sensing surface of LSPR-based biosensors using nanomaterials such as gold nanoparticles (AuNPs) and polymer to enhance surface absorption and sensitivity. The performance of this type of biosensors was evaluated on the application of small heavy metal molecule binding affinity study. This biosensor exhibited ~7 fold sensitivity enhancement and binding kinetics measurement capability comparing to traditional biosensors. Second, a miniaturized cell culture system was integrated into the LSPR-based biosensor system for the purpose of real-time biomarker signaling pathway studies and drug efficacy studies with living cells. To the best of our knowledge, this is the first LSPR-based sensing platform with the capability of living cell studies. We demonstrated the living cell measurement ability by studying the VEGF signaling pathway in living SKOV-3 cells. Results have shown that the VEGF secretion level from SKOV-3 cells is 0.0137 ± 0.0012 pg per cell. Moreover, we have demonstrated bevacizumab drug regulation to the VEGF signaling pathway using this biosensor. This sensing platform could potentially help studying biomolecular binding kinetics which elucidates the underlying mechanisms of biotransportation and drug delivery.