Studying biological science does not lead to adoption of a healthy lifestyle

Aims: The lifestyle and physical activity (PA) habits of young people play a key role in the prevention of cardiovascular and metabolic diseases at older ages. The current generation of biological science students at university holds promise for better future medicine and medical technology. However, their physical fitness and lifestyle are often ignored. Methods: Lifestyle, PAs and common risk factors for cardiovascular disease before, and at, university were collected from 408 students using self-completed, anonymous surveys between the academic years of 2017 and 2019 from the School of Biological Sciences, University of Reading. Statistical analysis was performed using SAS® 9.4 software. Results: Among the 408 participants, 134 were male and 274 were female with a mean (SD) age of 19.6 (2.24). Approximately 19% of participants consumed alcohol beyond the safe limit of <14 units/week (112 g/week). Among them, 65% were males. Before university, 47% of students failed to meet the UK National Physical Activity Guidelines (NPAG) which increased to 56% during university with males exhibiting a steeper incline. Compared to their lifestyles before university, more students had insufficient sleep and displayed greater sedentariness during university. Moreover, 16% of students declared no engagement in PA which was greater than the value of 12% before university. Fitness perceptions worsened by 11% during university particularly for females. Statistical analysis revealed that gender, BMI and fitness perceptions were significantly correlated with PA levels. The most prevalent explanation for inadequacy in meeting NPAG was insufficient time. Conclusion: Compared to their pre-university lifestyles, biological science students at university are more likely to adopt unhealthier behaviours with less time for exercise and prolonged sedentary behaviours, which increases the risk for cardiovascular diseases. It is important to raise awareness of their fitness perceptions and to encourage health-promoting programmes at university

Markers of inflammation and cardiovascular disease in recently diagnosed celiac disease patients

AIM: To evaluate novel risk factors and biomarkers of cardiovascular disease in celiac disease (CD) patients compared with healthy controls.METHODS:Twenty adult patients with recent diagnosis of CD and 20 sex, age and body mass index-matched healthy controls were recruited during a period of 12 mo.Indicators of carbohydrate metabolism, hematological parameters and high sensitive C reactive protein were determined. Moreover, lipoprotein metabolism was also explored through evaluation of the lipid profile and the activity of cholesteryl ester transfer protein and lipoprotein associated phospholipase A2, which is also considered a specific marker of vascular inflammation. The protocol was approved by the Ethic Committee from School of Pharmacy and Biochemistry, University of Buenos Aires and from Buenos Aires Italian Hospital, Buenos Aires, Argentina.RESULTS: Regarding the indicators of insulin resistance, CD patients showed higher plasma insulin levels [7.2 (5.0-11.3) mU/L vs 4.6 (2.6-6.7) mU/L, P < 0.05], increased Homeostasis Model Assessment-Insulin Resistance [1.45 (1.04-2.24) vs 1.00 (0.51-1.45), P < 0.05] and lower Quantitative Sensitive Check index [0.33 (0.28-0.40) vs 0.42 (0.34-0.65), P < 0.05] indexes. Folic acid concentration [5.4 (4.4-7.9) ng/mL vs 12.2 (8.0-14.2) ng/mL, P < 0.01] resulted to be lower and High-sensitivity C reactive protein levels higher (4.21 ± 6.47 mg/L vs 0.98 ± 1.13 mg/L, P < 0.01) in the patient group. With respect to the lipoprotein profile, CD patients showed lower high density lipoproteincholesterol (HDL-C) (45 ± 15 mg/dL vs 57 ± 17 mg/dL, P < 0.05) and apo A-I (130 ± 31 mg/dL vs 155 ± 29 mg/ dL, P < 0.05) levels, as well as higher total cholesterol/ HDL-C [4.19 (3.11-5.00) vs 3.52 (2.84-4.08), P < 0.05] and apo B/apo A-I (0.75 ± 0.25 vs 0.55 ± 0.16, P < 0.05) ratios in comparison with control subjects. No statistically significant differences were detectedin lipoprotein-associated lipid transfer protein and enzymes.CONCLUSION: The presence and interaction of the detected alterations in patients with CD, would constitute a risk factor for the development of atherosclerotic cardiovascular disease.Fil: Tetzlaff, Walter Francisco. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Meroño, Tomás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Menafra, Martín. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Martin, Maximiliano. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Botta, Eliana Elizabeth. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Matoso, María Dolores. Hospital Italiano; ArgentinaFil: Sorroche, Patricia Beatriz. Hospital Italiano; ArgentinaFil: De Paula, Juan A. Hospital Italiano; ArgentinaFil: Boero, Laura Estela. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; ArgentinaFil: Brites, Fernando Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Bioquímica Clínica; Argentin

Influence of Body Composition on Arterial Stiffness in Middle-Aged Adults: Healthy UAL Cross-Sectional Study

Background and objectives: Several anthropometric and body composition parameters have been linked to arterial stiffness (AS) as a biomarker of cardiovascular disease. However, little is known about which of these closely related factors is more strongly associated with AS. The aim of the present study was to analyze the relationship of different anthropometric and body composition parameters with AS in middle-aged adults. Materials and Methods: This cross-sectional study included 186 middle-aged participants (85 women, 101 men; age = 42.8 ± 12.6 years) evaluated as part of the Healthy UAL study, a population study conducted at the University of Almería with the main purpose of analyzing the etiology and risk factors associated with cardio-metabolic diseases. Anthropometric measures included neck, waist, and hip circumferences, as well as the waist-to-height ratio (WHtr). Bioimpedance-derived parameters included fat-free mass index (FFMI), fat mass index (FMI), and percent of body fat (%BF). AS was measured by pulse wave velocity (PWV). The relationships of interest were examined through stepwise regression analyses in which age and sex were also introduced as potential confounders. Results: Neck circumference (in the anthropometric model; R2: 0.889; β: age = 0.855, neck = 0.204) and FFMI (in the bio-impedance model; R2: 0.891; β: age = 0.906, FFMI = 0.199) emerged as significant cross-sectional predictors of AS. When all parameters were included together (both anthropometry and bio-impedance), both neck circumference and FFMI appeared again as being significantly associated with AS (R2: 0.894; β: age = 0.882, FFMI = 0.126, neck = 0.093). Conclusion: It was concluded that FFMI and neck circumference are correlated with AS regardless of potential confounders and other anthropometric and bioimpedance-derived parameters in middle-aged adults

Protocol of a Prospective Observational Study on the Relationship Between Glucose Fluctuation and Cardiovascular Events in Patients with Type 2 Diabetes

IntroductionA recent study demonstrated that large glucose fluctuations were associated with an increased incidence of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM) and acute myocardial infarction. However, it is unknown whether glucose fluctuations are related to the incidence of CVD or the progression of atherosclerosis in patients with T2DM with no apparent history of CVD. In this protocol, we will be investigating the relationships of glucose fluctuations evaluated by continuous glucose monitoring (CGM) to the incidence of composite cardiovascular events and the progression of atherosclerosis in patients with T2DM who had no apparent history of CVD.MethodsThis is a prospective, multicenter, 5-year follow-up observational study. Between April 2018 and October 2019, 1000 participants are expected to be recruited at 34 medical institutions. CGM using FreeStyle Libre Pro is useful for evaluating glucose fluctuations by continuously monitoring glucose levels in interstitial fluid for up to 14 days. The primary study outcome is the relationship between fluctuations in glucose levels evaluated by CGM and the incidence of composite cardiovascular events. Secondary outcomes include the relationships of fluctuations in glucose levels evaluated by CGM to changes in carotid intima media thickness evaluated by echography or grayscale median (an index of tissue characteristics of the carotid wall), brachial–ankle pulse wave velocity, development or progression of diabetic retinopathy or nephropathy, quality-of-life-related diabetes therapy, quality of sleep, development of dementia, and autonomic nerve function.Planned OutcomeThis protocol is designed to investigate the relationship between glucose fluctuations and the incidence of composite cardiovascular events. We completed the registration of 1000 participants in March 2019. Thus, results will be available in 2024. We expect that evaluating glucose fluctuations will aid the identification of patients with a high probability of developing CVD.Trial RegistrationClinicalTrials.gov identifier, UMIN000032325

Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

Exploring the diagnostic accuracy of the KidFit screening tool for identifying children with health and motor performance-related fitness impairments: A feasibility study

Child obesity is associated with poor health and reduced motor skills. This study aimed to assess the diagnostic accuracy of the KidFit Screening Tool for identifying children with overweight/obesity, reduced motor skills and reduced cardiorespiratory fitness. Fifty-seven children (mean age: 12.57 &plusmn; 1.82 years; male/female: 34/23) were analysed. The Speed and Agility Motor Screen (SAMS) and the Modified Shuttle Test-Paeds (MSTP) made up the KidFit Screening Tool. Motor Proficiency (BOT2) (Total and Gross) was also measured. BMI, peak-oxygen-uptake (VO2peak) were measured with a representative sub-sample (n = 25). Strong relationships existed between the independent variables included in the KidFit Screening Tool and; BMI (R2 = 0.779, p &lt; 0.001); Gross Motor Proficiency (R2 = 0.612, p &lt; 0.001) and VO2peak (mL/kg/min) (R2 = 0.754, p &lt; 0.001). The KidFit Screening Tool has a correct classification rate of 0.84 for overweight/obesity, 0.77 for motor proficiency and 0.88 for cardiorespiratory fitness. The sensitivity and specificity of the KidFit Screening Tool for identifying children with overweight/obesity was 100% (SE = 0.00) and 78.95%, respectively (SE = 0.09), motor skills in the lowest quartile was 90% (SE = 0.095) and 74.47% (SE = 0.064), respectively, and poor cardiorespiratory fitness was 100% (SE = 0.00) and 82.35% (SE = 0.093), respectively. The KidFit Screening Tool has a strong relationship with health- and performance-related fitness, is accurate for identifying children with health- and performance-related fitness impairments and may assist in informing referral decisions for detailed clinical investigations

Insomnia and mechanistic pathways to atherosclerotic CVD in HIV

Indiana University-Purdue University Indianapolis (IUPUI)Study 1: Background: Insomnia may be a risk factor for cardiovascular disease in HIV (HIV-CVD); however, mechanisms have yet to be elucidated. Methods: We examined cross-sectional associations of insomnia symptoms with biological mechanisms of HIV-CVD (immune activation, systemic inflammation, and coagulation) among 1,542 people living with HIV from the Veterans Aging Cohort Study (VACS) Biomarker Cohort. Past-month insomnia symptoms were assessed by the item, “Difficulty falling or staying asleep?,” with the following response options: “I do not have this symptom” or “I have this symptom and…” “it doesn’t bother me,” “it bothers me a little,” “it bothers me,” “it bothers me a lot.” Circulating levels of the monocyte activation marker soluble CD14 (sCD14), inflammatory marker interleukin-6 (IL-6), and coagulation marker D-dimer were determined from blood specimens. Demographic- and fully-adjusted (CVD risk factors, potential confounders, HIV-related factors) regression models were constructed, with log-transformed biomarker variables as the outcomes. We present the exponentiated regression coefficient (exp[b]) and its 95% confidence interval (CI). Results: For sCD14 and D-dimer, we observed no significant associations. For IL-6, veterans in the “bothers a lot” group had 15% higher IL-6 than veterans in the “I do not have this symptom” group in the demographic-adjusted model (exp[b]=1.15, 95%CI=1.02-1.29, p=.03). This association was nonsignificant in the fully-adjusted model (exp[b]=1.07, 95%CI=0.95-1.19, p=.25). Conclusion: We observed little evidence of relationships between insomnia symptoms and markers of biological mechanisms of HIV-CVD. Other mechanisms may be responsible for the insomnia-CVD relationship in HIV; however, future studies with comprehensive assessments of insomnia symptoms are warranted. Study 2: Background: While insomnia has been identified as a potential risk factor for cardiovascular disease in HIV (HIV-CVD), research on the underlying pathophysiological mechanisms is scarce. Methods: We examined associations between 0-to-12-week changes in sleep disturbance and the concurrent 0-to-12-week changes and the subsequent 12-to-24-week changes in markers of systemic inflammation, coagulation, and endothelial dysfunction among people living with HIV (n = 33-38) enrolled in a depression clinical trial. Sleep disturbance was measured using the Pittsburgh Sleep Quality Index. Inflammatory markers interleukin-6 (IL-6) and C-reactive protein (CRP) and coagulation marker D-dimer were determined from blood specimens; endothelial dysfunction marker brachial flow-mediated dilation (FMD) was determined by ultrasound. 0-to-12-week variables were calculated as 12-week visit minus baseline, and 12-to-24-week variables were calculated as 24-week minus 12-week. We constructed multivariate linear regression models for each outcome adjusting for age, sex, race/ethnicity, Framingham risk score, and baseline depressive symptoms. Results: We did not observe statistically significant associations between 0-to-12-week changes in sleep disturbance and 0-to-12-week or 12-to-24-week changes in IL-6, CRP, D-dimer, or FMD. However, we did observe potentially meaningful associations, likely undetected due to low power. For 0-to-12-weeks, every 1-standard deviation (SD) increase, or worsening, in the sleep disturbance change score was associated with a 0.41 pg/mL and 80 ng/mL decease in IL-6 and D-dimer, respectively. For 12-to-24-weeks, every 1-SD increase in sleep disturbance change score was associated with a 0.63 mg/L, 111 ng/mL, and 0.82% increase in CRP, D-dimer, and FMD, respectively. Conclusion: We observed potentially meaningful, though not statistically significant, associations between changes in sleep disturbance and changes in biological mechanisms underlying HIV-CVD over time. Some associations were in the expected direction, but others were not. Additional studies are needed that utilize larger samples and validated, comprehensive assessments of insomnia