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Cardiorespiratory Fitness is Associated with Glycated Hemoglobin and Triglyceride Levels in Severely Obese Men: A Retrospective Clinical Data Analysis
Abstract
Background Even subjects with severe obesity show a wide range of metabolic health states, with some showing marked alterations in glucose and lipid metabolism whereas others do not. In severely obese women, we could recently show that the degree of cardiorespiratory fitness is, independently of body mass and age, associated with several markers of glucose and lipid metabolism.
Aims In our retrospective study on a clinical data set, we questioned whether such an association also exists in severely obese men.
Methods Cardiorespiratory fitness, i. e. workload (Wpeak) and oxygen uptake (V̇O2,peak) at peak exercise, was assessed by a bicycle spiroergometry in 133 severely obese men (all BMI>35 kg m−2). The following metabolic blood markers were also measured: Fasting serum glucose, insulin, triglycerides (TG), total, low-, high-density cholesterol (Chol, LDL, HDL), uric acid, and whole blood glycated hemoglobin (HbA1c). The Chol/HDL ratio and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) were also calculated.
Results Multiple stepwise linear regression models including age, body mass, and smoking status as independent variables revealed that Wpeak and V̇O2,peak, explained 4.5 to 10.7% of variance in HbA1c and TG (all beta<− 0.22; all p<0.02). Including fat free mass instead of body mass in respective models revealed that both Wpeak and V̇O2,peak were predictors of HbA1c and TG (all beta<− 0.265; all p<0.013), respectively, while Wpeak also accounted for variance in glucose and Chol (both beta<− 0.259; both p<0.023).
Conclusions Similar to previous observations in women, our data indicate that cardiorespiratory fitness assessed by bicycle ergospirometry test is associated with glucose and lipid metabolism in severely obese men. The strength of the found associations suggest a mild to moderate influence of cardiorespiratory fitness on metabolic health in severe obesity.
... Based on the suggested relationship between CRF and lipid metabolism and the inflammatory response, we carried out additional mediation analyses on the laboratory data to investigate potential risks of low CRF resulting from PFAS exposure (Haufe et al. 2010;Lavie et al. 2011;Wilms et al. 2020). Specifically, we investigated the relationship of PFNA exposure and indices of lipid metabolism [total cholesterol (TC), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C)], and indices of inflammation [white blood cell (WBC) count and CRP]. ...
Exposure to perfluoroalkyl substances (PFASs) is associated with a number of adverse health outcomes. However, the relationship between mixed and individual PFAS exposure and cardiorespiratory fitness (CRF) in adolescents remains unclear. We used cross-sectional data from 491 teenagers (aged 13–19 years) from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) and examined the association between mixed PFAS exposure and CRF via weighted quantile sum (WQS) regression. Maximal oxygen consumption (VO2max) was used to evaluate CRF. Multivariate linear regression was performed to investigate the relationship between each PFAS and VO2max as well as the relationship between PFAS exposure and the inflammation parameters and blood lipid content. Mediation analyses were performed to investigate possible explanations of the risk of low CRF due to PFAS exposure. The results showed that for males, mixed PFAS exposure was negatively related to VO2max (beta = − 0.80, 95% CI: − 1.53 to − 0.10, P = 0.028) and that of the PFASs, perfluorononanoic acid (PFNA) had the greatest influence on VO2max. In the individual PFAS analysis, PFNA was negatively related to VO2max in male adolescents (beta = − 1.49, 95% CI: − 2.65 to − 0.32, P = 0.013). Additionally, significant relationships among serum PFNA levels and total cholesterol and the white blood cell (WBC) count were found. Mediation analyses revealed that WBC count accounted for 24.18% of the variation between PFNA level and CRF. The present results provide epidemiological evidence that exposure to PFASs, mainly PFNA, is negatively associated with CRF, possibly via alterations in WBC count.
We have investigated the differences between metabolically "healthy" morbidly obese patients and those with comorbidities.
Thirty-two morbidly obese patients were divided by the absence ("healthy": DM-DL-) or presence of comorbidities (dyslipidemic: DM-DL+, or dyslipidemic and with type 2 diabetes: DM+DL+). We have studied various plasma parameters and gene expression adipose tissue, before and after gastric bypass.
The group DM+DL+ tends to have lower values than the other two groups for anthropometric parameters. Regarding the satiety parameters, only leptin (p = 0.0024) showed a significant increase with comorbidities. Lipid parameters showed significant differences among groups, except for phospholipids and NEFA. For insulin resistance parameters, only glucose (p < 0.0001) was higher in DM+DL+ patients, but not insulin or homeostasis model assessment of insulin resistance (HOMA-IR). The gene expression of adiponectin, insulin receptor (INSR) and glucose receptor-4 (GLUT4), in the subcutaneous fat, decreased in all groups vs. a non-obese control. Interleukin-6 (IL6) and the inhibitor of plasminogen activator type 1 (PAI-1) genes decreased only in DM-DL+ and DM+DL+, but not in "healthy" patients. Leptin increased in all groups vs. the non-obese control. The visceral fat from DM+DL+ patients showed a sharp decrease in adiponectin, GLUT4, IL6 and PAI-1. All parameters mentioned above improved very significantly by surgery, independent of the occurrence of comorbidities.
The morbidly obese "healthy" individual is not really metabolically healthy, but morbidly obese individuals with diabetes and dyslipidemia are more metabolically imbalanced.
Mental fatigue is a psychobiological state caused by prolonged periods of demanding cognitive activity. Although the impact of mental fatigue on cognitive and skilled performance is well known, its effect on physical performance has not been thoroughly investigated. In this randomized crossover study, 16 subjects cycled to exhaustion at 80% of their peak power output after 90 min of a demanding cognitive task (mental fatigue) or 90 min of watching emotionally neutral documentaries (control). After experimental treatment, a mood questionnaire revealed a state of mental fatigue (P = 0.005) that significantly reduced time to exhaustion (640 +/- 316 s) compared with the control condition (754 +/- 339 s) (P = 0.003). This negative effect was not mediated by cardiorespiratory and musculoenergetic factors as physiological responses to intense exercise remained largely unaffected. Self-reported success and intrinsic motivation related to the physical task were also unaffected by prior cognitive activity. However, mentally fatigued subjects rated perception of effort during exercise to be significantly higher compared with the control condition (P = 0.007). As ratings of perceived exertion increased similarly over time in both conditions (P < 0.001), mentally fatigued subjects reached their maximal level of perceived exertion and disengaged from the physical task earlier than in the control condition. In conclusion, our study provides experimental evidence that mental fatigue limits exercise tolerance in humans through higher perception of effort rather than cardiorespiratory and musculoenergetic mechanisms. Future research in this area should investigate the common neurocognitive resources shared by physical and mental activity.
Recent guidelines for treatment of overweight and obesity include recommendations for risk stratification by disease conditions and cardiovascular disease (CVD) risk factors, but the role of physical inactivity is not prominent in these recommendations.
To quantify the influence of low cardiorespiratory fitness, an objective marker of physical inactivity, on CVD and all-cause mortality in normal-weight, overweight, and obese men and compare low fitness with other mortality predictors.
Prospective observational data from the Aerobics Center Longitudinal Study.
Preventive medicine clinic in Dallas, Tex.
A total of 25714 adult men (average age, 43.8 years [SD, 10.1 years]) who received a medical examination during 1970 to 1993, with mortality follow-up to December 31, 1994.
Cardiovascular disease and all-cause mortality based on mortality predictors (baseline CVD, type 2 diabetes mellitus, high serum cholesterol level, hypertension, current cigarette smoking, and low cardiorespiratory fitness) stratified by body mass index.
During the study period, there were 1025 deaths (439 due to CVD) during 258781 man-years of follow-up. Overweight and obese men with baseline CVD or CVD risk factors were at higher risk for all-cause and CVD mortality compared with normal-weight men without these predictors. Using normal-weight men without CVD as the referent, the strongest predictor of CVD death in obese men was baseline CVD (age- and examination year-adjusted relative risk [RR], 14.0; 95% confidence interval [CI], 9.4-20.8); RRs for obese men with diabetes mellitus, high cholesterol, hypertension, smoking, and low fitness were similar and ranged from 4.4 (95% CI, 2.7-7.1) for smoking to 5.0 (95% CI, 3.6-7.0) for low fitness. Relative risks for all-cause mortality in obese men ranged from 2.3 (95% CI, 1.7-2.9) for men with hypertension to 4.7 (95% CI, 3.6-6.1) for those with CVD at baseline. Relative risk for all-cause mortality in obese men with low fitness was 3.1 (95% CI, 2.5-3.8) and in obese men with diabetes mellitus 3.1 (95% CI, 2.3-4.2) and as slightly higher than the RRs for obese men who smoked or had high cholesterol levels. Low fitness was an independent predictor of mortality in all body mass index groups after adjustment for other mortality predictors. Approximately 50% (n = 1674) of obese men had low fitness, which led to a population-attributable risk of 39% for CVD mortality and 44% for all-cause mortality. Baseline CVD had population attributable risks of 51% and 27% for CVD and all-cause mortality, respectively.
In this analysis, low cardiorespiratory fitness was a strong and independent predictor of CVD and all-cause mortality and of comparable importance with that of diabetes mellitus and other CVD risk factors.
This study, examining the longitudinal relation among physical activity, body mass index, and development of type 2 diabetes in a high-risk population, is unique because diabetes was determined by oral glucose tolerance testing rather than by self-report. A physical activity questionnaire assessing past year leisure and occupational activity was administered to 1,728 nondiabetic Pima individuals aged 15-59 years as part of a series of clinic examinations in the Gila River Indian Community from 1987 to 2000. During an average follow-up period of 6 years, 346 subjects developed diabetes. Using time-dependent Cox proportional hazards modeling adjusting for age, the authors found that total activity was related to diabetes incidence in women and men (p < 0.05 in women only). After additional adjustment for body mass index, the relation between activity and diabetes incidence was weakened in both men and women. When the age-adjusted diabetes incidence rates were examined by levels of activity stratified by tertile of body mass index, the diabetes incidence rate remained lower in more active than in less active men and women from all body mass index groups, with the exception of the middle body mass index tertile in men (p < 0.05 in women only). These results suggest that the adoption and maintenance of a physically active lifestyle can play a significant role in preventing type 2 diabetes.
The role of physical activity in preventing obesity during adolescence remains unknown. We examined changes in activity in relation to changes in body-mass index (BMI) and adiposity in a cohort of 1152 black and 1135 white girls from the USA, who were followed up prospectively from ages 9 or 10 to 18 or 19 years.
BMI and sum of skinfold thickness were assessed annually, whereas habitual activity was assessed at years 1 (baseline), 3, 5, and 7-10. Each girls' overall activity status was categorised as active, moderately active, or inactive. Longitudinal regression models examined associations between changes in activity and in overall activity status with changes in BMI and in sum of skinfold thickness.
Each decline in activity of 10 metabolic equivalent [MET]-times per week was associated with an increase in BMI of 0.14 kg/m2 (SE 0.03) and in sum of skinfold thickness of 0.62 mm (0.17) for black girls, and of 0.09 kg/m2 (0.02) and 0.63 mm (0.13) for white girls. At ages 18 or 19 years, BMI differences between active and inactive girls were 2.98 kg/m2 (p<0.0001) for black girls and 2.10 kg/m2 (p<0.0001) for white girls. Similar results were apparent for sum of skinfold thickness. For moderately active girls, changes in BMI and sum of skinfold thickness were about midway between those for active and inactive girls.
Changes in activity levels of US girls during adolescence significantly affected changes in BMI and adiposity. Thus, preventing the steep decline in activity during adolescence is an important method to reduce obesity.
Aims
Both severe obesity and type 2 diabetes (T2DM) are associated with reduced pulmonary function and reduced cardiorespiratory fitness. We investigated whether T2DM further aggravates the impaired pulmonary function and cardiorespiratory fitness in subjects with severe obesity.
Methods
In this cross-sectional study pulmonary function (forced expiratory volume within 1 s, FEV1; vital capacity, VC) was assessed in 65 severely obese subjects with T2DM (T2DM group) and 65 severely obese subjects without T2DM (non-T2DM group), pairwise matched for sex, age, weight, and height. In 30 of the matched pairs, cardiorespiratory fitness was assessed by an incremental bicycle test (peak workload, Wpeak, oxygen uptake, V̇O2,peak).
Results
FEV1 and VC did not differ between the T2DM and non-T2DM group (all p≥0.110), whereas Wpeak and V̇O2,peak - absolute values as well as relative to body mass - were significantly lower in subjects with T2DM compared to those without T2DM (all p≤0.030). Objective markers of maximal exertion, i. e., maximal heart rate and respiratory exchange ratio, did not differ between the 2 groups (both p≥0.245).
Conclusions
The presence of T2DM in subjects with severe obesity is associated with lower cardiorespiratory fitness but with no further reduction in pulmonary function compared to subjects with severe obesity but without T2DM. While the cause-effect relationship of this association is not yet clear, these findings highlight the interplay between cardiorespiratory fitness and metabolic health, even in subjects with severe obesity.
In this review, the influence of fat depots on insulin resistance and the main characteristics of metabolically obese normal-weight and metabolically healthy obese phenotypes are discussed. Medline/PubMed and Science Direct were searched for articles related to the terms metabolically healthy obesity, metabolically obese normal weight, adipose tissue, and insulin resistance. Normal weight and obesity might be heterogeneous in regard to their effects. Fat distribution and lower insulin sensitivity are the main factors defining phenotypes within the same body mass index. Although these terms are interesting, controversies about them remain. Future studies exploring these phenotypes will help elucidate the roles of adiposity and/or insulin resistance in the development of metabolic alterations.
© The Author(s) 2015. Published by Oxford University Press on behalf of the International Life Sciences Institute. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Introduction:
Many studies have shown an inverse relationship between cardio-respiratory fitness and cardio-metabolic risk markers in normal-weight to moderately obese subjects. However, whether such a relationship exists in severely obese subjects is not known.
Materials and Methods:
Cardio-respiratory fitness was measured by bicycle spiroergometry in 308 severely obese women (all BMI>35 kg/m²). The following cardio-metabolic risk markers were assessed: Glycolized hemoglobin levels (HbA1c), fasting glucose, insulin, calculated HOMA index, triglycerides (TG), total, low-, high-density cholesterol (Chol, LDL; HDL), Chol/HDL-Ratio, and uric acid. Computed multiple stepwise linear regression models generally included age, weight and height as independent variables.
Results:
Multiple stepwise linear regression models indicated that peak but not aerobic threshold related cardio-respiratory fitness indices were independently of age, weight and height associated with several cardio-metabolic risk markers. Specifically, maximally achieved load (Watt-peak) explained 1.4% of the variance in glucose levels (beta=−0.13; p=0.04) and 2.8% of the variance in HbA1c levels (beta=−0.18; p=0.01), while maximally achieved O2-uptake explained 3.9% of the variance in TG levels (beta=−0.20, p=0.001).
Conclusion:
Our data for the first time indicate that cardio-respiratory fitness is independently associated with cardio-metabolic risk markers in severely obese women.
During the past decade, skeletal muscle has been identified as a secretory organ. Accordingly, we have suggested that cytokines and other peptides that are produced, expressed and released by muscle fibres and exert either autocrine, paracrine or endocrine effects should be classified as myokines. The finding that the muscle secretome consists of several hundred secreted peptides provides a conceptual basis and a whole new paradigm for understanding how muscles communicate with other organs, such as adipose tissue, liver, pancreas, bones and brain. However, some myokines exert their effects within the muscle itself. Thus, myostatin, LIF, IL-6 and IL-7 are involved in muscle hypertrophy and myogenesis, whereas BDNF and IL-6 are involved in AMPK-mediated fat oxidation. IL-6 also appears to have systemic effects on the liver, adipose tissue and the immune system, and mediates crosstalk between intestinal L cells and pancreatic islets. Other myokines include the osteogenic factors IGF-1 and FGF-2; FSTL-1, which improves the endothelial function of the vascular system; and the PGC-1α-dependent myokine irisin, which drives brown-fat-like development. Studies in the past few years suggest the existence of yet unidentified factors, secreted from muscle cells, which may influence cancer cell growth and pancreas function. Many proteins produced by skeletal muscle are dependent upon contraction; therefore, physical inactivity probably leads to an altered myokine response, which could provide a potential mechanism for the association between sedentary behaviour and many chronic diseases.
The purpose of this systematic review was to study the relative health risks of poor cardio-respiratory fitness (or physical inactivity) in normal-weight people vs. obesity in individuals with good cardio-respiratory fitness (or high physical activity). The core inclusion criteria were: publication year 1990 or later; adult participants; design prospective follow-up, case-control or cross-sectional; data on cardio-respiratory fitness and/or physical activity; data on BMI (body mass index), waist circumference or body composition; outcome data on all-cause mortality, cardiovascular disease mortality, cardiovascular disease incidence, type 2 diabetes or cardiovascular and type 2 diabetes risk factors. Thirty-six publications filled the criteria for inclusion. The data indicate that the risk for all-cause and cardiovascular mortality was lower in individuals with high BMI and good aerobic fitness, compared with individuals with normal BMI and poor fitness. In contrast, having high BMI even with high physical activity was a greater risk for the incidence of type 2 diabetes and the prevalence of cardiovascular and diabetes risk factors, compared with normal BMI with low physical activity. The conclusions of the present review may not be applicable to individuals with BMI > 35.
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
Maximal VO2 (VO2max) has mostly been the province of exercise physiologists wishing to provide a measure of athletic potential or to characterize subjects in exercise-related research. It is also used clinically to determine a patient's exercise capacity. More recently, it has been recognized that the study of VO2max can provide fundamental insight into O2 transport at all points between inspired air and muscle mitochondria. This review focuses on understanding how VO2max is set and concludes that the more athletic one is, the more VO2max is sensitive to O2 transport conductances in the lungs, circulation, and skeletal muscle. These transport conductances form an integrated system, all components interacting to define VO2max. A particularly important component is diffusive conductance in muscle. This appears to be abnormal in chronic conditions such as obstructive pulmonary disease and heart and renal failure and may well explain why correction of central cardiovascular defects in O2 transport in such patients fails to restore exercise capacity.
In the exercising human, maximal oxygen uptake (VO2max) is limited by the ability of the cardiorespiratory system to deliver oxygen to the exercising muscles. This is shown by three major lines of evidence: 1) when oxygen delivery is altered (by blood doping, hypoxia, or beta-blockade), VO2max changes accordingly; 2) the increase in VO2max with training results primarily from an increase in maximal cardiac output (not an increase in the a-v O2 difference); and 3) when a small muscle mass is overperfused during exercise, it has an extremely high capacity for consuming oxygen. Thus, O2 delivery, not skeletal muscle O2 extraction, is viewed as the primary limiting factor for VO2max in exercising humans. Metabolic adaptations in skeletal muscle are, however, critical for improving submaximal endurance performance. Endurance training causes an increase in mitochondrial enzyme activities, which improves performance by enhancing fat oxidation and decreasing lactic acid accumulation at a given VO2. VO2max is an important variable that sets the upper limit for endurance performance (an athlete cannot operate above 100% VO2max, for extended periods). Running economy and fractional utilization of VO2max also affect endurance performance. The speed at lactate threshold (LT) integrates all three of these variables and is the best physiological predictor of distance running performance.
The aim of this study was to assess the nature and magnitude of the differences in submaximal and maximal exercise capacity parameters between lean and obese women. A total of 225 healthy obese women 18-65 years (BMI> or=30 kg/m(2)) and 81 non-athletic lean women (BMI< or=26 kg/m(2)) were selected. Anthropometric measurements (weight and height), body composition assessment (bioelectrical impedance method) and a maximal exercise capacity test on a bicycle ergometer were performed. Oxygen uptake (VO(2)), carbon dioxide production (VCO(2)), expired ventilation (VE), respiratory quotient (RQ), breathing efficiency (VE/VO(2)), mechanical efficiency (ME) and anaerobic threshold (AT) were calculated. At a submaximal intensity load of 70 W, VO(2) (l/min) was larger in the obese women and was already 78% of their peak VO(2), whereas in the non-obese it was only 69% (P=0.0001). VE (l/min) was larger, VE/VO(2) did not differ and ME was lower in obese compared to the lean women. AT occurred at the same percentage of peak VO(2) in both lean and obese women. At peak effort, achieved load, terminal VO(2) (l min(-1) kg(-1)), VE, heart rate, RQ respiratory exchange ratio and perceived exertion were lower in obese subjects compared to the non-obese. Obese subjects mentioned significantly more musculoskeletal pain as a reason to end the test, whereas in lean subjects it was leg fatigue. Lean women recovered better as after 2 min they were already at 35% of the peak VO(2), whereas in the obese women it was 47% (P=0.0001). Our results confirm that exercise capacity is decreased in obesity, both at submaximal and peak intensity, and during recovery. Moreover, at peak effort musculoskeletal pain was an important reason to end the test and not true leg fatigue. These findings are important when designing exercise programs for obese subjects.
The relative size of the effects of fitness and fatness on longevity has been studied in only one cohort. The authors examined this issue using data from 2,506 women and 2,860 men in the Lipid Research Clinics Study. The mean age was 46.6 years in women and 45.1 years in men at baseline (1972-1976). Fitness was assessed using a treadmill test, and fatness was assessed as body mass index calculated from measured height and weight. Participants were followed for vital status through 1998. Hazard ratios were calculated using proportional hazard models that included covariates for age, education, smoking, alcohol intake, and the dietary Keys score. Fitness and fatness were both associated with mortality from all causes and from cardiovascular disease. For mortality from all causes, the adjusted hazard ratios were 1.32 among the fit-fat, 1.30 among the unfit-not fat, and 1.57 among the unfit-fat women compared with fit-not fat women. Among men the same hazard ratios were 1.25, 1.44, and 1.49 [corrected]. There were no significant interactions between fitness and fatness in either men or women. The authors conclude that both fitness and fatness are risk factors for mortality, and that being fit does not completely reverse the increased risk associated with excess adiposity.
Whether higher levels of physical activity can counteract the elevated risk of death associated with adiposity is controversial.
We examined the associations of the body-mass index and physical activity with death among 116,564 women who, in 1976, were 30 to 55 years of age and free of known cardiovascular disease and cancer.
During 24 years of follow-up, 10,282 deaths occurred--2370 from cardiovascular disease, 5223 from cancer, and 2689 from other causes. Mortality rates increased monotonically with higher body-mass-index values among women who had never smoked (P for trend <0.001). In combined analyses of all participants, adiposity predicted a higher risk of death regardless of the level of physical activity. Higher levels of physical activity appeared to be beneficial at all levels of adiposity but did not eliminate the higher risk of death associated with obesity. As compared with women who were lean (i.e., they had a body-mass index lower than 25) and active (they spent 3.5 or more hours exercising per week), the multivariate relative risks of death were 1.55 (95 percent confidence interval, 1.42 to 1.70) for lean and inactive women, 1.91 (95 percent confidence interval, 1.60 to 2.30) for women who were obese (defined as a body-mass index of 30 or higher) but active, and 2.42 (95 percent confidence interval, 2.14 to 2.73) for inactive, obese women. Even modest weight gain during adulthood, independent of physical activity, was associated with a higher risk of death. We estimate that excess weight (defined as a body-mass index of 25 or higher) and physical inactivity (less than 3.5 hours of exercise per week) together could account for 31 percent of all premature deaths, 59 percent of deaths from cardiovascular disease, and 21 percent of deaths from cancer among nonsmoking women.
Both increased adiposity and reduced physical activity are strong and independent predictors of death.
The comparative importance of physical inactivity and obesity as predictors of coronary heart disease (CHD) risk remains unsettled.
We followed 88 393 women, 34 to 59 years of age, in the Nurses' Health Study from 1980 to 2000. These participants did not have cardiovascular disease and cancer at baseline. We documented 2358 incident major CHD events (including nonfatal myocardial infarction and fatal CHD) during 20 years of follow-up, including 889 cases of fatal CHD and 1469 cases of nonfatal myocardial infarction. In a multivariate model adjusting for cardiovascular risk factors, overweight and obesity were significantly associated with increased risk of CHD, whereas increasing levels of physical activity were associated with a graded reduction in CHD risk (P<0.001). In joint analyses of body mass index (BMI) and physical activity in women who had a healthy weight (BMI, 18.5 to 24.9 kg/m2) and were physically active (exercise > or =3.5 h/wk) as the reference group, the relative risks of CHD were 3.44 (95% confidence interval [CI], 2.81 to 4.21) for women who were obese (BMI > or =30 kg/m2) and sedentary (exercise <1 h/wk), 2.48 (95% CI, 1.84 to 3.34) for women who were active but obese, and 1.48 (95% CI, 1.24 to 1.77) for women who had a healthy weight but were sedentary. In combined analyses of waist-hip ratio and physical activity, both waist-hip ratio and physical activity were significant predictors of CHD, and the highest risk was among women in the lowest category of physical activity and the highest tertile of waist-hip ratio (relative risk=3.03; 95% CI, 1.96 to 4.18). Even a modest weight gain (4 to 10 kg) during adulthood was associated with 27% (95% CI, 12% to 45%) increased risk of CHD compared with women with a stable weight after adjusting for physical activity and other cardiovascular risk factors.
Obesity and physical inactivity independently contribute to the development of CHD in women. These data underscore the importance of both maintaining a healthy weight and regular physical activity in preventing CHD.
To investigate whether cardiovascular mortality related to obesity could be modified by physical activity.
Mortality follow-up.
Population study.
Participants in a health survey: 34 868 women and 32 872 men free from known cardiovascular disease or diabetes at baseline.
Total cardiovascular mortality.
During 16 years of follow-up, 3026 women and 3526 men had died from cardiovascular causes. In middle age, obesity [body mass index (BMI) of 30 or higher] was associated with increased risk of cardiovascular death, but the association weakened with age. After 70, there was no association between BMI and cardiovascular death. At all ages, a lower level of physical activity was associated with a higher cardiovascular mortality. In women with high physical activity, indicated by at least 30 min of moderate to vigorous activity more than once a week, cardiovascular mortality was only slightly higher in the obese compared to lean women (adjusted relative risk, 1.27; 95% confidence interval, 0.80-2.00). In men with high physical activity, cardiovascular mortality was, however, significantly higher among the obese (relative risk, 1.62; 95% confidence interval, 1.09-2.40). In both genders cardiovascular mortality was substantially higher in obese people who reported no regular physical activity compared to obese people with a high level of physical activity.
In obese women, being highly active may, to a large extent, compensate for the risk-increasing effect of being obese, whereas in obese men who engage in a high level of physical activity, the risk of cardiovascular death may be higher than in lean and equally active men.
The relative contribution of adiposity and physical inactivity to the risk of developing type 2 diabetes remains controversial.
We prospectively examined the individual and joint association of obesity and physical activity with the development of type 2 diabetes in 68,907 female nurses who had no history of diabetes, cardiovascular disease, or cancer at baseline. Adiposity was measured by BMI and waist circumference. Physical activity was assessed through average hours of moderate or vigorous exercise and computation of an MET score.
We documented 4,030 incident cases of type 2 diabetes during 16 years of follow-up (from 1986 to 2002). In a multivariate model including age, smoking, and other diabetes risk factors, risk of type 2 diabetes increased progressively with increasing BMI (P < 0.001) and waist circumference (P < 0.001) and with decreasing physical activity levels (P < 0.001). In joint analyses of BMI and physical activity, using women who had a healthy weight (BMI < 25 kg/m(2)) and were physically active (exercise > or = 21.8 MET h/week) as the reference group, the relative risks of type 2 diabetes were 16.75 (95% CI 13.99-20.04) for women who were obese (BMI > or = 30 kg/m(2)) and inactive (exercise < 2.1 MET h/week), 10.74 (8.74-13.18) for women who were active but obese, and 2.08 (1.66-2.61) for women who were lean but inactive. In combined analyses of waist circumference and physical activity, both variables were significant predictors of type 2 diabetes, but the association for waist circumference was substantially stronger than that for physical inactivity.
Obesity and physical inactivity independently contribute to the development of type 2 diabetes; however, the magnitude of risk contributed by obesity is much greater than that imparted by lack of physical activity.