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Effects of Living High-Training Low and High on Body Composition and Metabolic Risk Markers in Overweight and Obese Females. BIOMED RESEARCH INTERNATIONAL 2020; 2020:3279710. [PMID: 32104687 PMCID: PMC7036094 DOI: 10.1155/2020/3279710] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 01/14/2020] [Accepted: 01/21/2020] [Indexed: 12/13/2022]
Abstract
This study examined the effects of 4 weeks of living high-training low and high (LHTLH) under moderate hypoxia on body weight, body composition, and metabolic risk markers of overweight and obese females. Nineteen healthy overweight or obese females participated in this study. Participants were assigned to the normoxic training group (NG) or the LHTLH group (HG). The NG participants lived and trained at sea level. The HG participants stayed for approximately 10 hours in a simulated 2300 m normobaric state of hypoxia for six days a week and trained for 2 hours 3 times a week under the same simulated hypoxia. The interventions lasted for 4 weeks. All groups underwent dietary restriction based on resting metabolic rate. The heart rate of the participants was monitored every ten minutes during exercise to ensure that the intensity was in the aerobic range. Compared with the preintervention values, body weight decreased significantly in both the NG and the HG (−8.81 ± 2.09% and −9.09 ± 1.15%, respectively). The fat mass of the arm, leg, trunk, and whole body showed significant reductions in both the NG and the HG, but no significant interaction effect was observed. The percentage of lean soft tissue mass loss in the total body weight loss tended to be lower in the HG (27.61% versus 15.94%, P=0.085). Between the NG and the HG, significant interaction effects of serum total cholesterol (−12.66 ± 9.09% versus −0.05 ± 13.36%,) and apolipoprotein A1 (−13.66 ± 3.61% versus −5.32 ± 11.07%, P=0.042) were observed. A slight increase in serum high-density lipoprotein cholesterol (HDL-C) was observed in the HG (1.12 ± 12.34%) but a decrease was observed in the NG (−11.36 ± 18.91%). The interaction effect of HDL-C between NG and HG exhibited a significant trend (P=0.055). No added effects on serum triglycerides (TGs), low-density lipoprotein cholesterol (LDL-C), or APO-B were observed after 4 weeks of LHTLH. In conclusion, 4 weeks of LHTLH combined with dietary restriction could effectively reduce the body weight and body fat mass of overweight and obese females. Compared with training and sleeping under normoxia, no additive benefit of LHTLH on the loss of body weight and body fat mass was exhibited. However, LHTLH may help to relieve the loss of lean soft tissue mass and serum HDL-C.
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Sanghera DK, Bejar C, Sharma S, Gupta R, Blackett PR. Obesity genetics and cardiometabolic health: Potential for risk prediction. Diabetes Obes Metab 2019; 21:1088-1100. [PMID: 30667137 PMCID: PMC6530772 DOI: 10.1111/dom.13641] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 01/17/2019] [Accepted: 01/19/2019] [Indexed: 02/06/2023]
Abstract
The increasing burden of obesity worldwide and its effect on cardiovascular disease (CVD) risk is an opportunity for evaluation of preventive approaches. Both obesity and CVD have a genetic background and polymorphisms within genes which enhance expression of variant proteins that influence CVD in obesity. Genome-based prediction may therefore be a feasible strategy, but the identification of genetically driven risk factors for CVD manifesting as clinically recognized phenotypes is a major challenge. Clusters of such risk factors include hyperglycaemia, hypertension, ectopic liver fat, and inflammation. All involve multiple genetic pathways having complex interactions with variable environmental influences. The factors that make significant contributions to CVD risk include altered carbohydrate homeostasis, ectopic deposition of fat in muscle and liver, and inflammation, with contributions from the gut microbiome. A futuristic model depends on harnessing the predictive power of plausible genetic variants, phenotype reversibility, and effective therapeutic choices based on genotype-phenotype interactions. Inverting disease phenotypes into ideal cardiovascular health metrics could improve genetic and epigenetic assessment, and form the basis of a future model for risk detection and early intervention.
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Affiliation(s)
- Dharambir K. Sanghera
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- (Corresponding authors) Dharambir K. Sanghera, Ph.D., F.A.H.A., Department of Pediatrics, Section of Genetics, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm. D317 BMSB, Oklahoma City, OK 73104, USA, , Piers R. Blackett, M.D., Department of Pediatrics, Section of Endocrinology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA,
| | - Cynthia Bejar
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Sonali Sharma
- Department of Biochemistry, College of Medical Sciences, Rajasthan University of Health Sciences, Kumbha Marg, Pratap Nagar, Jaipur 302033, India
| | - Rajeev Gupta
- Academic Research Development Unit, College of Medical Sciences, Rajasthan University of Health Sciences, Kumbha Marg, Pratap Nagar, Jaipur 302033, India
| | - Piers R. Blackett
- Department of Pediatrics, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
- (Corresponding authors) Dharambir K. Sanghera, Ph.D., F.A.H.A., Department of Pediatrics, Section of Genetics, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm. D317 BMSB, Oklahoma City, OK 73104, USA, , Piers R. Blackett, M.D., Department of Pediatrics, Section of Endocrinology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA,
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Changes in non-fasting concentrations of blood lipids after a daily Chinese breakfast in overweight subjects without fasting hypertriglyceridemia. Clin Chim Acta 2019; 490:147-153. [PMID: 30615853 DOI: 10.1016/j.cca.2019.01.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 12/07/2018] [Accepted: 01/03/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Overweight is always accompanied by hypertriglyceridemia (HTG), but the change in non-fasting triglyceride (TG) concentration in overweight subjects without postprandial hypertriglyceridemia was unknown. METHODS Concentrations of serum lipids were measured at 2 and 4 h in matched overweight (OW group, n = 54) and control subjects (CON group, n = 55) after a daily meal. Concentrations of remnant cholesterol and non-HDL cholesterol were calculated according to the formulas. The diagnostic criteria for non-fasting HTG were based on 2 different consensus statement. ROC curve was used to determine the pointcut of postprandial HTG. RESULTS OW group had higher fasting concentrations of RC and non-HDL-C than CON group. Non-fasting concentrations of triglyceride and RC significantly increased in 2 groups while were higher in OW group (p < .05). The proportion of non-fasting HTG increased after a daily meal in OW group was significantly higher than the percentage of fasting HTG (p < .05). There was a significant correlation between the postprandial concentrations of TG and RC. CONCLUSIONS Overweight subjects were more likely to develop non-fasting hypertriglyceridemia and higher concentrations of RC and non-HDL-C. Additionally, 2.0 mmol/l at 4 h after breakfast could be a pointcut value to detect changes in lipid profile of Chinese overweight people.
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