Gary O'Donovan

The effects of high-intensity interval training on glucose regulation and insulin resistance: a meta-analysis

The aim of this meta‐analysis was to quantify the effects of high‐intensity interval training (HIIT) on markers of glucose regulation and insulin resistance compared with control conditions (CON) or continuous training (CT). Databases were searched for HIIT interventions based upon the inclusion criteria: training ≥2 weeks, adult participants and outcome measurements that included insulin resistance, fasting glucose, HbA1c or fasting insulin. Dual interventions and participants with type 1 diabetes were excluded. Fifty studies were included. There was a reduction in insulin resistance following HIIT compared with both CON and CT (HIIT vs. CON: standardized mean difference [SMD] = −0.49, confidence intervals [CIs] −0.87 to −0.12, P = 0.009; CT: SMD = −0.35, −0.68 to −0.02, P = 0.036). Compared with CON, HbA1c decreased by 0.19% (−0.36 to −0.03, P = 0.021) and body weight decreased by 1.3 kg (−1.9 to −0.7, P < 0.001). There were no statistically significant differences between groups in other outcomes overall. However, participants at risk of or with type 2 diabetes experienced reductions in fasting glucose (−0.92 mmol L−1, −1.22 to −0.62, P < 0.001) compared with CON. HIIT appears effective at improving metabolic health, particularly in those at risk of or with type 2 diabetes. Larger randomized controlled trials of longer duration than those included in this meta‐analysis are required to confirm these results.

Association of “Weekend Warrior” and Other Leisure Time Physical Activity Patterns With Risks for All-Cause, Cardiovascular Disease, and Cancer Mortality

Importance More research is required to clarify the association between physical activity and health in “weekend warriors” who perform all their exercise in 1 or 2 sessions per week. Objective To investigate associations between the weekend warrior and other physical activity patterns and the risks for all-cause, cardiovascular disease (CVD), and cancer mortality. Design, Setting, and Participants This pooled analysis of household-based surveillance studies included 11 cohorts of respondents to the Health Survey for England and Scottish Health Survey with prospective linkage to mortality records. Respondents 40 years or older were included in the analysis. Data were collected from 1994 to 2012 and analyzed in 2016. Exposures Self-reported leisure time physical activity, with activity patterns defined as inactive (reporting no moderate- or vigorous-intensity activities), insufficiently active (reporting <150 min/wk in moderate-intensity and <75 min/wk in vigorous-intensity activities), weekend warrior (reporting ≥150 min/wk in moderate-intensity or ≥75 min/wk in vigorous-intensity activities from 1 or 2 sessions), and regularly active (reporting ≥150 min/wk in moderate-intensity or ≥75 min/wk in vigorous-intensity activities from ≥3 sessions). The insufficiently active participants were also characterized by physical activity frequency. Main Outcomes and Measures All-cause, CVD, and cancer mortality ascertained from death certificates. Results Among the 63 591 adult respondents (45.9% male; 44.1% female; mean [SD] age, 58.6 [11.9] years), 8802 deaths from all causes, 2780 deaths from CVD, and 2526 from cancer occurred during 561 159 person-years of follow-up. Compared with the inactive participants, the hazard ratio (HR) for all-cause mortality was 0.66 (95% CI, 0.62-0.72) in insufficiently active participants who reported 1 to 2 sessions per week, 0.70 (95% CI, 0.60-0.82) in weekend warrior participants, and 0.65 (95% CI, 0.58-0.73) in regularly active participants. Compared with the inactive participants, the HR for CVD mortality was 0.60 (95% CI, 0.52-0.69) in insufficiently active participants who reported 1 or 2 sessions per week, 0.60 (95% CI, 0.45-0.82) in weekend warrior participants, and 0.59 (95% CI, 0.48-0.73) in regularly active participants. Compared with the inactive participants, the HR for cancer mortality was 0.83 (95% CI, 0.73-0.94) in insufficiently active participants who reported 1 or 2 sessions per week, 0.82 (95% CI, 0.63-1.06) in weekend warrior participants, and 0.79 (95% CI, 0.66-0.94) in regularly active participants. Conclusions and Relevance Weekend warrior and other leisure time physical activity patterns characterized by 1 or 2 sessions per week may be sufficient to reduce all-cause, CVD, and cancer mortality risks regardless of adherence to prevailing physical activity guidelines.

A non-exercise testing method for estimating cardiorespiratory fitness: associations with all-cause and cardiovascular mortality in a pooled analysis of eight population-based cohorts.

AIMS Cardiorespiratory fitness (CRF) is a key predictor of chronic disease, particularly cardiovascular disease (CVD), but its assessment usually requires exercise testing which is impractical and costly in most health-care settings. Non-exercise testing cardiorespiratory fitness (NET-F)-estimating methods are a less resource-demanding alternative, but their predictive capacity for CVD and total mortality has yet to be tested. The objective of this study is to examine the association of a validated NET-F algorithm with all-cause and CVD mortality. METHODS AND RESULTS The participants were 32,319 adults (14,650 men) aged 35-70 years who took part in eight Health Survey for England and Scottish Health Survey studies between 1994 and 2003. Non-exercise testing cardiorespiratory fitness (a metabolic equivalent of VO2max) was calculated using age, sex, body mass index (BMI), resting heart rate, and self-reported physical activity. We followed participants for mortality until 2008. Two thousand one hundred and sixty-five participants died (460 cardiovascular deaths) during a mean 9.0 [standard deviation (SD) = 3.6] year follow-up. After adjusting for potential confounders including diabetes, hypertension, smoking, social class, alcohol, and depression, a higher fitness score according to the NET-F was associated with a lower risk of mortality from all-causes (hazard ratio per SD increase in NET-F 0.85, 95% confidence interval: 0.78-0.93 in men; 0.88, 0.80-0.98 in women) and CVD (men: 0.75, 0.63-0.90; women: 0.73, 0.60-0.92). Non-exercise testing cardiorespiratory fitness had a better discriminative ability than any of its components (CVD mortality c-statistic: NET-F = 0.70-0.74; BMI = 0.45-0.59; physical activity = 0.60-0.64; resting heart rate = 0.57-0.61). The sensitivity of the NET-F algorithm to predict events occurring in the highest risk quintile was better for CVD (0.49 in both sexes) than all-cause mortality (0.44 and 0.40 for men and women, respectively). The specificity for all-cause and CVD mortality ranged between 0.80 and 0.82. The net reclassification improvement of CVD mortality risk (vs. a standardized aggregate score of the modifiable components of NET-F) was 27.2 and 21.0% for men and women, respectively. CONCLUSION The CRF-estimating method NET-F that does not involve exercise testing showed consistent associations with all-cause and cardiovascular mortality, and it had good discrimination and excellent risk reclassification improvement. As such, it merits further attention as a practical and potentially and useful risk prediction tool.

The association between neighbourhood greenspace and type 2 diabetes in a large cross-sectional study

Objective To investigate the relationship between neighbourhood greenspace and type 2 diabetes. Design Cross-sectional. Setting 3 diabetes screening studies conducted in Leicestershire, UK in 2004–2011. The percentage of greenspace in the participants home neighbourhood (3 km radius around home postcode) was obtained from a Land Cover Map. Demographic and biomedical variables were measured at screening. Participants 10 476 individuals (6200 from general population; 4276 from high-risk population) aged 20–75 years (mean 59 years); 47% female; 21% non-white ethnicity. Main outcome measure Screen-detected type 2 diabetes (WHO 2011 criteria). Results Increased neighbourhood greenspace was associated with significantly lower levels of screen-detected type 2 diabetes. The ORs (95% CI) for screen-detected type 2 diabetes were 0.97 (0.80 to 1.17), 0.78 (0.62 to 0.98) and 0.67 (0.49 to 0.93) for increasing quartiles of neighbourhood greenspace compared with the lowest quartile after adjusting for ethnicity, age, sex, area social deprivation score and urban/rural status (Ptrend=0.01). This association remained on further adjustment for body mass index, physical activity, fasting glucose, 2 h glucose and cholesterol (OR (95% CI) for highest vs lowest quartile: 0.53 (0.35 to 0.82); Ptrend=0.01). Conclusions Neighbourhood greenspace was inversely associated with screen-detected type 2 diabetes, highlighting a potential area for targeted screening as well as a possible public health area for diabetes prevention. However, none of the risk factors that we considered appeared to explain this association, and thus further research is required to elicit underlying mechanisms. Trial registration number This study uses data from three studies (NCT00318032, NCT00677937, NCT00941954).

Cardiorespiratory fitness and risk of type 2 diabetes mellitus: A 23-year cohort study and a meta-analysis of prospective studies

AIMS To investigate the association between cardiorespiratory fitness (CRF) and type 2 diabetes mellitus (T2DM) in a cohort of middle-age Finnish men and to summarise the current evidence in a meta-analysis of prospective studies. METHODS CRF was measured at baseline in a random population-based sample of 2520 subjects by assessing oxygen uptake during maximal exercise. Cox regression analysis was used to estimate the association between CRF, expressed as metabolic equivalents (METs), and the risk of T2DM adjusted for potential confounders; this estimate was then pooled with the results of other prospective studies in a meta-analysis. RESULTS Mean (SD) baseline age and CRF were 53 (5) years and 8.7 (2.1) METs, respectively. During 23 years of follow-up, 153 (6.1%) participants developed T2DM. The hazard ratio per 1-MET higher CRF, adjusted for age, body mass index, systolic blood pressure, serum HDL-cholesterol, and family history of T2DM, was 0.93 (95% confidence interval (CI): 0.84, 1.02; p = 0.109); further adjustment for smoking, education, and socioeconomic status did not materially change the estimate. In a random-effects meta-analysis of eight studies (92,992 participants and 8564 T2DM cases) combining maximally adjusted estimates, the pooled risk ratio of T2DM per 1-MET higher CRF level was 0.95 (95% CI: 0.93, 0.98; p = 0.003; I(2) = 81%), corresponding to 23 fewer cases per 100,000 person-years based on the assumption of a causal link between CRF and T2DM. CONCLUSIONS These data suggest that there is an inverse relationship between CRF and T2DM that is largely independent of other risk factors.

Objectively measured physical activity, cardiorespiratory fitness and cardiometabolic risk factors in the Health Survey for England.

OBJECTIVES The study aims to test the hypothesis that physical activity (PA) and cardiorespiratory fitness (CRF) are associated with cardiometabolic risk factors; and to test the hypothesis that CRF modifies (changes the direction and/or strength of) the associations between PA and cardiometabolic risk factors. METHODS PA and CRF were objectively measured in the 2008 Health Survey for England and the present study included 536 adults who completed at least 4 min of the eight-minute sub-maximal step test and wore an accelerometer for at least 10 h on at least four days. Linear regression models were fitted to examine the relationship between PA and cardiometabolic risk factors and between CRF and cardiometabolic risk factors. A test of interaction was performed to examine whether CRF modifies the associations between PA and cardiometabolic risk factors. RESULTS PA and CRF were associated with HDL cholesterol, the ratio of total to HDL cholesterol, glycated haemoglobin and BMI after adjustment for potential confounders. There was little evidence that CRF changed the direction or strength of associations between PA and cardiometabolic risk factors. CONCLUSIONS PA and CRF are associated with cardiometabolic risk factors. A larger sample is required to determine if CRF modifies associations between PA and cardiometabolic risk factors.

Fatness, fitness, and cardiometabolic risk factors in middle-aged white men

The objective was to test the hypothesis that traditional and novel cardiometabolic risk factors would be significantly different in groups of men of different fatness and fitness. Total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, glucose, insulin, high-sensitivity C-reactive protein, alanine aminotransferase, aspartate aminotransferase, γ-glutamyltransferase, leptin, adiponectin, tumor necrosis factor-α, interleukin-6, interleukin-10, fibrinogen, and insulin resistance were assessed in 183 nonsmoking white men aged 35 to 53 years, including 62 who were slim and fit (waist girth ≤90 cm and maximal oxygen consumption [VO(2)max] above average), 24 who were slim and unfit (waist girth ≤90 cm and VO(2)max average or below), 39 who were fat and fit (waist girth ≥100 cm and VO(2)max above average), and 19 who were fat and unfit (waist girth ≥100 cm and VO(2)max average or below). Seventy-six percent gave blood on 2 occasions, and the average of 1 or 2 blood tests was used in statistical tests. Waist girth (centimeters) and fitness (milliliters of oxygen per kilogram of fat-free mass) were associated with high-density lipoprotein cholesterol, leptin, and insulin resistance after adjustment for age, saturated fat intake, and total energy intake. High-density lipoprotein cholesterol, triglycerides, alanine aminotransferase, and insulin resistance were significantly different in men who were fat and fit and those who were fat and unfit. These data suggest that differences in lipid and lipoprotein concentrations, liver function, and insulin resistance may explain why the risks of chronic disease are lower in men who are fat and fit than those who are fat and unfit.

Association Between Lifestyle Factors and the Incidence of Multimorbidity in an Older English Population.

Background Evidence on the role of lifestyle factors in relation to multimorbidity, especially in elderly populations, is scarce. We assessed the association between five lifestyle factors and incident multimorbidity (presence of ≥2 chronic conditions) in an English cohort aged ≥50 years. Methods We used data from waves 4, 5, and 6 of the English Longitudinal Study of Ageing. Data on smoking, alcohol consumption, physical activity, fruit and vegetable consumption, and body mass index were extracted and combined to generate a sum of unhealthy lifestyle factors for each individual. We examined whether these lifestyle factors individually or in combination predicted multimorbidity during the subsequent wave. We used marginal structural Cox proportional hazard models, adjusted for both time-constant and time-varying factors. Results A total of 5,476 participants contributed 232,749 person-months of follow-up during which 1,156 cases of incident multimorbidity were recorded. Physical inactivity increased the risk of multimorbidity by 33% (adjusted hazard ratio [aHR]: 1.33, 95% confidence interval [CI]: 1.03-1.73). The risk was about two to three times higher when inactivity was combined with obesity (aHR: 2.87, 95% CI: 1.55-5.31) or smoking (aHR: 2.35, 95% CI: 1.36-4.08) and about four times when combined with both (aHR: 3.98, 95% CI: 1.02-17.00). Any combination of 2, 3, and 4 or more unhealthy lifestyle factors significantly increased the multimorbidity hazard, compared with none, from 42% to 116%. Conclusion This study provides evidence of a temporal association between combinations of different unhealthy lifestyle factors with multimorbidity. Population level interventions should include reinforcing positive lifestyle changes in the population to reduce the risk of developing multimorbidity.

The association between air pollution and type 2 diabetes in a large cross-sectional study in Leicester: The CHAMPIONS Study

BACKGROUND Observational evidence suggests there is an association between air pollution and type 2 diabetes; however, there is high risk of bias. OBJECTIVE To investigate the association between air pollution and type 2 diabetes, while reducing bias due to exposure assessment, outcome assessment, and confounder assessment. METHODS Data were collected from 10,443 participants in three diabetes screening studies in Leicestershire, UK. Exposure assessment included standard, prevailing estimates of outdoor nitrogen dioxide and particulate matter concentrations in a 1×1km area at the participants home postcode. Three-year exposure was investigated in the primary analysis and one-year exposure in a sensitivity analysis. Outcome assessment included the oral glucose tolerance test for type 2 diabetes. Confounder assessment included demographic factors (age, sex, ethnicity, smoking, area social deprivation, urban or rural location), lifestyle factors (body mass index and physical activity), and neighbourhood green space. RESULTS Nitrogen dioxide and particulate matter concentrations were associated with type 2 diabetes in unadjusted models. There was no statistically significant association between nitrogen dioxide concentration and type 2 diabetes after adjustment for demographic factors (odds: 1.08; 95% CI: 0.91, 1.29). The odds of type 2 diabetes was 1.10 (95% CI: 0.92, 1.32) after further adjustment for lifestyle factors and 0.91 (95% CI: 0.72, 1.16) after yet further adjustment for neighbourhood green space. The associations between particulate matter concentrations and type 2 diabetes were also explained away by demographic factors. There was no evidence of exposure definition bias. CONCLUSIONS Demographic factors seemed to explain the association between air pollution and type 2 diabetes in this cross-sectional study. High-quality longitudinal studies are needed to improve our understanding of the association.

Relationships between exercise, smoking habit and mortality in more than 100,000 adults

Exercise is associated with reduced risks of all‐cause, cardiovascular disease (CVD) and cancer mortality; however, the benefits in smokers and ex‐smokers are unclear. The aim of this study was to investigate associations between exercise, smoking habit and mortality. Self‐reported exercise and smoking, and all‐cause, CVD and cancer mortality were assessed in 106,341 adults in the Health Survey for England and the Scottish Health Survey. There were 9149 deaths from all causes, 2839 from CVD and 2634 from cancer during 999,948 person‐years of follow‐up. Greater amounts of exercise were associated with decreases and greater amounts of smoking were associated with increases in the risks of mortality from all causes, CVD and cancer. There was no statistically significant evidence of biological interaction; rather, the relative risks of all‐cause mortality were additive. In the subgroup of 26,768 ex‐smokers, the all‐cause mortality hazard ratio was 0.70 (95% CI 0.60, 0.80), the CVD mortality hazard ratio was 0.71 (0.55, 092) and the cancer mortality hazard ratio was 0.66 (0.52, 0.84) in those who exercised compared to those who did not. In the subgroup of 28,440 smokers, the all‐cause mortality hazard ratio was 0.69 (0.57, 0.83), the CVD mortality hazard ratio was 0.66 (0.45, 0.96) and the cancer mortality hazard ratio was 0.69 (0.51, 0.94) in those who exercised compared to those who did not. Given that an outright ban is unlikely, this study is important because it suggests exercise reduces the risks of all‐cause, CVD and cancer mortality by around 30% in smokers and ex‐smokers.