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Yandiswa Y. Yako, Mogamat S. Hassan, Rajiv T. Erasmus, Lize van der Merwe, Susan Janse van Rensburg, and Tandi Edith Matsha

Background:

There is evidence demonstrating that the contribution of sedentary behavior and effect of physical activity on metabolic phenotypes is mediated by polymorphisms in genes.

Methods:

The type and frequency of physical activity was assessed by means of structured questionnaires in 1555 South African school learners. Anthropometric measurements, blood pressure, fasting blood glucose and lipids were measured using standard procedures. The effect of different types and frequency of physical activity on obesity-related traits was assessed in relation to MC3R T6K and V81I genotypes in 430 of the learners.

Results:

Levels of total cholesterol were significantly lower in learners carrying the MC3R T6K and V81I minor alleles, after adjusting for age, race, gender, and each specific physical activity category. An activity-by-genotype interaction was also detected: learners heterozygous for the V81I polymorphism and performed house chores often had reduced total cholesterol. Though no association was observed between frequency of physical activity and BMI, television viewing was significantly associated with an increase in height, weight and marginally with waist circumference.

Conclusion:

Our findings suggest that physical activity even in the form of house chores has a positive effect on metabolic traits and this effect is further enhanced in the presence of MC3R polymorphisms.

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Hugo Ribeiro Zanetti, Lucas Gonçalves da Cruz, Camilo Luís Monteiro Lourenço, Giovana Castilho Ribeiro, Marco Aurélio Ferreira de Jesus Leite, Fernando Freitas Neves, Mário Leon Silva-Vergara, and Edmar Lacerda Mendes

Background:

Highly active antiretroviral therapy (HAART) is associated with high total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), triglyceride (TG), and C-reactive protein (CRP) levels. The natural course of the HIV infection reduces the high-density lipoprotein level (HDL-c). Thus, physical exercise plays a key role in reducing the effects of HAART and HIV.

Methods:

Thirty people living with HIV (PLHIV) were randomized to the nonlinear resistance training (NLRT) and control (CON) groups. The NLRT group underwent 12 weeks of resistance training, whereas the CON group maintained usual daily activities. All volunteers underwent anthropometric, body composition, and biochemical assessments at the beginning and end of 12 weeks.

Results:

After 12 weeks, the NLRT group had increased lean body mass (P < .0001), and a reduction in body fat mass (P < .0001) and body fat percentage (P < .0001). The levels of TC (P < .0001), LDL-c (P = .049), TG (P < .0001), and CRP (P = .004) were reduced, and the HDL-c level increased (P < .0001).

Conclusion

Twelve weeks of NLRT causes beneficial changes in the body composition, lipid profile, and inflammation markers in PLHIV, and it can be used in this patient population.

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Susan Sullivan Glenney, Derrick Paul Brockemer, Andy C. Ng, Michael A. Smolewski, Vladimir M. Smolgovskiy, and Adam S. Lepley

effects of exercise training on cardiovascular health. Traditional serum biomarkers, such as total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), glucose, C-reactive protein (CRP), insulin, and triglyceride levels, have been used to study the effects of exercise interventions

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Anna Witkowska, Małgorzata Grabara, Dorota Kopeć, and Zbigniew Nowak

were collected in Excel software. The lipid profile measurements included total cholesterol level (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) contents measured in the hospital’s analytical laboratory (AU480 Chemistry Analyzer

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Inès Boukabous, Alexis Marcotte-Chénard, Taha Amamou, Pierre Boulay, Martin Brochu, Daniel Tessier, Isabelle Dionne, and Eléonor Riesco

containing ethylenediaminetetraacetic acid or a coagulation activator gel (depending on the variables measured). Fasting lipid profile (triglycerides, total cholesterol, high-density lipoprotein cholesterol [HDL-C], non-HDL-C, and low-density lipoprotein cholesterol [LDL-C]) as well as glucose and insulin

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Rochelle R. Costa, Adriana C.K. Buttelli, Leandro Coconcelli, Laura F. Pereira, Alexandra F. Vieira, Alex de O. Fagundes, Juliano B. Farinha, Thais Reichert, Ricardo Stein, and Luiz F.M. Kruel

Dyslipidemias are heterogeneous disorders of lipid metabolism arising from multiple etiologies that result in alterations in blood lipoproteins (low-density lipoprotein—LDL and high-density lipoprotein—HDL) and lipid (total cholesterol—TC and triglycerides—TG) concentrations. 1 In elderly women

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Evelin Lätt, Jarek Mäestu, and Jaak Jürimäe

Blood Samples Venous blood samples were taken after an overnight fast and stored at −80°C before analysis. Total cholesterol (in millimoles per liter), high-density lipoprotein (mmol/L), low-density lipoprotein (in millimoles per liter), and triglycerides (TRG; in millimoles per liter) were analyzed by

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Waynne F. Faria, Filipe R. Mendonça, Géssika C. Santos, Sarah G. Kennedy, Rui G.M. Elias, and Antonio Stabelini Neto

of Clinical Pathology/Laboratory Medicine ( 40 ). Blood was collected between 7:30  and 9:00 AM after fasting for at least 8 hours. Blood samples were processed and analyzed on the same day as collection. The enzymatic colorimetric method was adopted for the analysis of total cholesterol (TC

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Rochelle Rocha Costa, Adriana Cristine Koch Buttelli, Alexandra Ferreira Vieira, Leandro Coconcelli, Rafael de Lima Magalhães, Rodrigo Sudatti Delevatti, and Luiz Fernando Martins Kruel

the effects of isolated ST on lipid (total cholesterol [TC], HDL, low-density lipoprotein [LDL], and TG) and inflammatory marker profiles (CRP and adiponectin) in adults with the use of a control group. Methods This systematic review was conducted using a predetermined protocol established according

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Alireza Paahoo, Vahid Tadibi, and Nasser Behpoor

cholesterol; TC, total cholesterol; TG, triglycerides. *Significant difference versus pretest ( P  < .01). **Significant difference versus pretest ( P  < .001). ***Significant difference between the changes ( P  < .01). † Significant difference between the changes ( P  < .001). There were also significant