scholarly journals Association and Interaction Effect of AGTR1 and AGTR2 Gene Polymorphisms with Dietary Pattern on Metabolic Risk Factors of Cardiovascular Disease in Malaysian Adults

Nutrients ◽  
2017 ◽  
Vol 9 (8) ◽  
pp. 853 ◽  
Author(s):  
Roseline Yap ◽  
Yoshihiro Shidoji ◽  
Wai Yap ◽  
Motofumi Masaki
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Interaction Effect ◽  
Dietary Pattern ◽  
Gene Polymorphisms ◽  
Metabolic Risk Factors ◽  
Metabolic Risk ◽  
Agtr2 Gene

scholarly journals Plasma leptin concentrations: gender differences and associations with metabolic risk factors for cardiovascular disease

Diabetologia ◽  
1997 ◽  
Vol 40 (10) ◽  
pp. 1178-1184 ◽  
Author(s):  
C. Couillard ◽  
P. Mauriège ◽  
D. Prud'homme ◽  
A. Nadeau ◽  
A. Tremblay ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Gender Differences ◽  
Metabolic Risk Factors ◽  
Metabolic Risk ◽  
Plasma Leptin

scholarly journals Ten-Year Risk of Fatal Cardiovascular Disease and its Association with Metabolic Risk Factors Among Waste Pickers in South Africa

2021 ◽  
Author(s):  
Felix Made ◽  
Engelbert A Nonterah ◽  
Nonhlanhla Tlotleng ◽  
Vusi Ntlebi ◽  
Nisha Naicker
Keyword(s):  
Risk Factors ◽  
South Africa ◽  
Cardiovascular Disease ◽  
Ordinary Least Squares ◽  
Metabolic Risk Factors ◽  
Metabolic Risk ◽  
Least Squares Regression ◽  
Unhealthy Lifestyle ◽  
Informal Workers ◽  
Waste Pickers

Abstract Background Cardiovascular disease (CVD) is the leading cause of death among non-communicable diseases in South Africa. Several metabolic risk factors contribute to the development of CVD. Informal workers such as waste pickers could be unhealthy lifestyle naive, and most public health research on CVD does not include this understudied population. This study estimated the 10-year risk of fatal CVD and its association with metabolic risk factors in an understudied study population of waste pickers in Johannesburg, South Africa. MethodsA cross-sectional survey was conducted among waste pickers in two landfill sites in Johannesburg. We used the Systematic Coronary Risk Evaluation (SCORE) risk charts to estimate the 10-year risk of fatal CVD. We then employed ordinary least squares regression to assess the association between the 10-year risk of fatal CVD with metabolic risk factors. Other variables adjusted in the regression model were HIV status, education, income, injuries from work, clinic visits in the previous 12 months, and alcohol consumption. ResultsA total of 370 waste pickers were included in this analysis, 265 (73.41%) were males. The mean age of the participants was 34 years. The majority were between the age of 20 and 39 years. More than 55% of the waste pickers did not visit a clinic in the previous 12 months, and 68.57% were smoking. The 10-year survival probability from CVD was more than 99% for both males and females. Waste pickers who were overweight/obese, hypertensive, or with an elevated blood glucose level had high statistically significant mean percentages of the 10-year risk of fatal CVD compared to those who did not have the metabolic risk factors. Conclusions Prevention of 10-year risk of fatal CVD in this understudied population of waste pickers should target the control of obesity, hypertension, and diabetes. Health awareness and education for waste pickers will be an important step in reducing the burden of these metabolic risk factors. We further recommend that health systems should recognize waste pickers as a high-risk group and consider extensive CVDs surveillance.

Abstract 37: Metabolomic Signatures of Metabolic Risk Factors for Atherosclerotic Cardiovascular Disease

2012 ◽  
Vol 32 (suppl_1) ◽  
Author(s):  
Xiaoyan Yin ◽  
Shih-Jen Hwang ◽  
Aram Adourian ◽  
Paul Courchesne ◽  
Neal Gordon ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Glutamic Acid ◽  
Metabolic Risk Factors ◽  
Individual Risk ◽  
Atherosclerotic Cardiovascular Disease ◽  
Metabolic Risk ◽  
Sample Mean ◽  
Total N ◽  
Main Effects

Background: Multiple cardiovascular disease (CVD) risk factors cluster in the same individuals and their concurrence is used to diagnose metabolic syndrome (MetSyn), which carries substantial risk for CVD. We hypothesized that MetSyn is associated with multiple metabolomic derangements. Methods: As part of the SABRe CVD initiative, a multi-project investigation of biomarkers of CVD and its risk factors, we designed a 2x2x2 factorial study of MetSyn risk factors that included 650 individuals from the Framingham Heart Study (out of a total N of ∼3200) assigned to 8 unique groups of approximately 81 individuals each, sampled from high vs. low strata of BMI, lipids, and glucose. We conducted gas chromatography-mass spectroscopy (GC/MS) on plasma samples from 650 eligible individuals. General linear modeling was used to identify biomarkers that differed across all 8 groups or differed in their main effects on individual risk factors. Results: Characteristics of the study sample (mean±SD), according to group assignment, are presented in the Table. GC/MS characterized 149 metabolites; of these 18 differed across all groups at P<5x10 -8 and 36 differed at P<0.00001. The top 3 most highly significant metabolites across all groups were glucose (P=2x10 -40 ), glutamic acid (P=4x10 -26 ), and sphingomyelins (lowest P=8x10 -25 ). The top 3 most highly significant main effects of metabolites for BMI were: glutamic acid (P=2x10 -18 ), sitosterol (P=2x10 -10 ), and uric acid (P=3x10 -10 ), for dyslipidemia: sphingomyelins (lowest P=1x10 -27 ), glutamic acid (P=5x10 -20 ), and lactic acid (P=7x10 -12 ), and for dysglycemia: glucose (P=1x10 -42 ), fructose (P=3x10 -7 ), and 2-hydroxybutanoic acid (P=6x10 -7 ). Conclusions: Metabolomic profiling identified multiple biomarker signatures of MetSyn and its major metabolic risk factors. These novel findings warrant external replication. Understanding the pathways represented by our results may help to unravel the molecular derangements contributing to MetSyn and its constituent risk factors. This knowledge may identify therapeutic targets for the prevention and treatment of CVD.

Abstract 18258: High-intensity Interval Training Improves Aerobic Capacity and Metabolic Risk Factors in Older Adults: A Randomized Controlled Trial

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Chueh-Lung Hwang ◽  
Jeung-Ki Yoo ◽  
Han-Kyul Kim ◽  
Moon-Hyon Hwang ◽  
Eileen M Handberg ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Older Adults ◽  
Oxygen Consumption ◽  
Aerobic Capacity ◽  
Metabolic Risk Factors ◽  
Peak Oxygen Consumption ◽  
Control Group ◽  
Metabolic Risk ◽  
Maximal Heart Rate

Introduction: Aging is associated with increased cardiovascular disease risk including reduced aerobic capacity and increased metabolic risk factors. Aerobic exercise training improves the risk for cardiovascular disease. The optimal exercise regimen for older adults, however, remains unknown. We tested the hypothesis that high-intensity interval training (HIIT) would be more effective than isocaloric moderate-intensity continuous training (MICT) in improving aerobic capacity and metabolic risks factors in older adults. Methods: Thirty eight sedentary older adults (age 65±1.0 yrs; mean±SE), free of cardiovascular disease, were randomly assigned to HIIT (n=13), MICT (n=13) or non-exercise control group (n=12). HIIT consisted of cycling at alternating intensities of 90 and 70% maximal heart rate for 40 minutes while MICT consisted of cycling at 70% maximal heart rate for 47 minutes. HIIT and MICT were performed on Airdyne bicycles, 4 days/week for 8 weeks, under supervision. Aerobic capacity was assessed by peak oxygen consumption during an incremental treadmill test. Metabolic risk factors included fasting lipid profile, glucose, insulin, and insulin resistance (homeostasis model assessment; HOMA-IR). Body composition was assessed using dual-energy x-ray absorptiometry. All measures were obtained at baseline and following the intervention. Results: Peak oxygen consumption increased by 2.8 ml/kg/min (23.0±1.6 vs. 25.7±1.5, pre- vs. post-intervention; P <0.05) in HIIT, but did not change in MICT and control group (26.0±1.5 vs. 26.2±1.4 and 23.5±1.4 vs. 24.0±1.4, respectively; P >0.05). Insulin and HOMA-IR improved only in HIIT (-1.9±0.6μU/ml and -0.42±0.16, P <0.05; respectively) and these changes were negatively associated with the change in peak oxygen consumption (r=-0.37, P <0.05 for both). Overall, lipids, body weight, body fat, and fat-free mass did not change in response to the intervention in any of the groups (P>0.05). Conclusions: HIIT but not MICT improves aerobic capacity and glucose control in healthy older adults without affecting lipids or body composition.

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