Non-exercise equations to estimate fitness in white European and South Asian men

© 2015 American College of Sports Medicine PURPOSE: Cardiorespiratory fitness is a strong, independent predictor of health, whether it is measured in an exercise test or estimated in an equation. The purpose of this study was to develop and validate equations to estimate fitness in middle-aged white European and South Asian men. METHODS: Multiple linear regression models (n=168, including 83 white European and 85 South Asian men) were created using variables that are thought to be important in predicting fitness (VO2 max, mL⋅kg⋅min): age (years); BMI (kg·m); resting heart rate (beats⋅min); smoking status (0=never smoked, 1=ex or current smoker); physical activity expressed as quintiles (0=quintile 1, 1=quintile 2, 2=quintile 3, 3=quintile 4, 4=quintile 5), categories of moderate- to vigorous-intensity physical activity (0=150-225 min⋅wk, 3=>225-300 min⋅wk, 4=>300 min⋅wk), or minutes of moderate- to vigorous-intensity physical activity (min⋅wk); and, ethnicity (0=South Asian, 1=white). The leave-one-out-cross-validation procedure was used to assess the generalizability and the bootstrap and jackknife resampling techniques were used to estimate the variance and bias of the models. RESULTS: Around 70% of the variance in fitness was explained in models with an ethnicity variable, such as: VO2 max = 77.409 - (age*0.374) – (BMI*0.906) – (ex or current smoker*1.976) + (physical activity quintile coefficient) – (resting heart rate*0.066) + (white ethnicity*8.032), where physical activity quintile 1 is 1, 2 is 1.127, 3 is 1.869, 4 is 3.793, and 5 is 3.029. Only around 50% of the variance was explained in models without an ethnicity variable. All models with an ethnicity variable were generalizable and had low variance and bias. CONCLUSION: These data demonstrate the importance of incorporating ethnicity in non-exercise equations to estimate cardiorespiratory fitness in multi-ethnic populations

The Forest Plot for the effect of TNFi on QUICKI.

<p>The Forest Plot for the effect of TNFi on QUICKI.</p

Funnel plots (A) for HOMA from 8 selected studies and (B) for QUICKI from 4 selected studies evaluating effects of TNFi on IR/IS.

<p>Funnel plots (A) for HOMA from 8 selected studies and (B) for QUICKI from 4 selected studies evaluating effects of TNFi on IR/IS.</p

Flow chart of studies identification and selection.

<p>Flow chart of studies identification and selection.</p

Additional file 1: of Metabolic characterization of menopause: cross-sectional and longitudinal evidence

Supplementary tables and figures in support our results and findings. (DOCX 187 kb

Variance explained by physical activity of at least moderate intensity, performed in bouts of at least 10 minutes (MPA_bouts), and derived value for MPA_bouts in South Asian men equivalent to 150 min.week⁻¹ of MPA_bouts in European men, for glucose, insulin resistance, lipid, blood pressure and overall cardio-metabolic risk factors.

<p>Values are mean (95% CI). Factors represent single summary variables derived from factor analysis. The glycaemia factor includes fasting glucose and HbA1c; the insulin resistance factor includes insulin, HDL cholesterol, triglycerides; the lipid factor includes total cholesterol, HDL cholesterol, triglycerides; the blood pressure factor includes systolic and diastolic blood pressure; and the overall cardio-metabolic risk factor includes glucose, HbA1c, insulin, total cholesterol, HDL cholesterol, triglycerides and systolic and diastolic blood pressure. Models were adjusted for age, BMI, daily accelerometer wear time, and number of days of accelerometer wear.</p

Additional file 1: Table S1. of Association of maternal diabetes/glycosuria and pre-pregnancy body mass index with offspring indicators of non-alcoholic fatty liver disease

Univariable associations of maternal diabetes status, by maternal existing diabetes, gestational diabetes and glycosuria compared to no diabetes/glycosuria with offspring USS and blood-based markers of non-alcoholic fatty liver disease. Table S2. Results of the multivariable model (model 4) of the association of maternal diabetes/glycosuria with offspring USS determined fatty liver. Table S3. Results of the multivariable model (model 4) of the association of maternal pre-pregnancy obesity status and BMI with offspring USS determined fatty liver. Table S4. Multivariable associations (model 4, with adjustment for offspring concurrent BMI) of maternal diabetes/glycosuria with offspring USS and blood-based markers of non-alcoholic fatty liver disease. (N = 1,215 or 2,358 as indicated). Table S5. Multivariable associations of maternal pre-pregnancy BMI ((model 4, with adjustment for offspring concurrent BMI)) with offspring USS and blood-based markers of non-alcoholic fatty liver disease. (N = 1,215 or 2,358 as indicated). (DOCX 26 kb

Relationship between the overall cardio-metabolic risk factor and level of physical activity of at least moderate intensity, performed in bouts of at least 10 minutes (MPA_bouts) in South Asians (solid red line) and Europeans (solid blue line).

<p>266 min.week⁻¹ of MPA_bouts in South Asians gave an equivalent cardio-metabolic risk factor level to that observed in Europeans undertaking 150 min.week⁻¹ of MPA_bouts. Dotted red lines represent the 95% confidence bands around the regression line for South Asians. These bands were used to calculate the 95% CI around the equivalent level of MPA_bouts in South Asians. Regression lines are adjusted for age, BMI, daily accelerometer wear time, and number of days of accelerometer wear.</p

Descriptive characteristics of the cohort.

<p>Descriptive characteristics of the cohort.</p

Glucose measurement on admission by patient characteristics.

a<p>All figures are counts (%) except for hospital, for which the proportion with glucose measures across hospitals was summarised using the median (interquartile range) and [range].</p