Is NAFLD Associated With High Levels of Sedentary Behavior?
Is NAFLD Associated With High Levels of Sedentary Behavior?
Thirty-seven adults with clinically defined NAFLD were recruited to the study from the subspecialty NAFLD clinic at the Freeman Hospital, Newcastle upon Tyne, UK. These were unrelated patients with NAFLD, derived from a patient population originally identified as having ultrasonographically detected bright liver and abnormal liver biochemical tests. Alternate diagnoses were excluded, including increased alcohol intake (males and females consuming greater than 21/14 units of alcohol per week (>30/20 g/day ethanol), respectively), chronic viral hepatitis (hepatitis B and hepatitis C), autoimmune liver diseases, hereditary haemochromatosis, α1-antitrypsin deficiency, Wilson's disease and drug-induced liver disease. NAFLD was defined as >5% liver fat content on H-MRS. Further exclusion criteria included: implanted ferrous metal; insulin sensitising treatment or dietary change (for people with T2DM, diet and metformin, were acceptable for inclusion if stable for 6 months). Patients had no physical restriction for exercise determined by an exercise test. The control group were individually matched to patients by age, sex and within 3 BMI from a control sample of over 1000 healthy people screened by questionnaire to be free of any metabolic disease.
The study protocol was approved by County Durham and Tees Valley 2 Research Ethics Committee. All participants provided written informed consent. Visits were undertaken at the Clinical Research Facility, Royal Victoria Infirmary, or the Magnetic Resonance Centre, both in Newcastle upon Tyne, UK.
Magnetic resonance studies were performed using a 3.0 Tesla Philips Achieva scanner (Philips Medical Systems, Best, The Netherlands). Liver fat was measured by localised H-MRS (PRESS, TR/TR=3000 ms/35 ms, 3×3×3 cmvoxel, SENSE torso Array). Blinded quantification of the spectra (water and CH2 resonances) was performed using the java-based magnetic resonance user interface (jMRUI V.3.0). Following manual first and second-order phase correction, spectra were analysed using a non-linear least squares algorithm (AMARES). Liver fat was expressed as a percentage of liver volume, corrected for proton density of water and lipid.
Physical activity and energy expenditure were assessed objectively using a multisensor array (SenseWear Pro3, Bodymedia, Pennsylvania, USA) previously validated in healthy adults. Volunteers were asked to wear the armband on their right upper arm for 7 days. All subjects were instructed to remove the armband only for bathing/showering purposes or any water-based activity. A subject's multisensor array data were acceptable for analysis if overall wear-time was ≥80% of the total time that they had the monitor in situ.
The following matrices of physical activity were derived from the multisensor array as units per day: total energy expenditure (TEE); active energy expenditure (AEE); average metabolic equivalents (MET); duration of physical activity (>3.0 METs); duration of moderate physical activity (3.0–5.9 METs); duration of vigorous activity (6.0–9.0 METs); duration of very vigorous activity (≥9.0 METs); number of steps; and duration of monitor worn.
Total sedentary duration was classed as total time spent in activities ≤2.9 METs, excluding sleep. Patterns of sedentary behaviour were assessed by power law analyses of the lengths of sedentary bouts fitted from raw sedentary data, as described in more detail previously. Briefly, the density p(x) of sedentary bouts in a time bin width d(x) was plotted against the bout length x on a logarithmic scale to derive power distribution (equation 1) from the shape of the histogram with respect to their length
The type of sedentary distribution characterised by the exponent α (equation 2), can quantify different sedentary behaviour strategies, with a lower α indicating that subjects accumulate sedentary time with a larger proportion of long sedentary bouts:
From these power distributions, Lorentz curves were calculated where the fraction Wx of the total sedentary time that is accumulated in bouts longer than any sedentary period of length x:
The curves are then plotted as Wx/p(x) pairs for each patient and control. Activity patterns were also assessed by assessing transitions from being inactive to active, and normalised by the length of the recording, termed 'Sedentary to Active Transitions'. These data are presented as a percentage of the activity data per day.
Volunteers completed the validated International Physical Activity Questionnaire (IPAQ) to determine levels of physical activity and sit time after wearing the monitor for 7 days. The IPAQ includes four activity domains: job-related physical activity, transportation, housework (including house maintenance and caring for the family), recreation and leisure time activity. The IPAQ was scored using the guidelines produced by The IPAQ Group (http://www.ipaq.ki.se/scoring.pdf).
Bodyweight (kg) and height (cm) were measured using an electronic scale and stadiometer, respectively, (SECA, Birmingham, UK).
In the NAFLD group, a blood sample was taken from a forearm vein following an overnight fast (>8 h). Whole blood glucose was measured immediately (YSI 2300 Stat Plus-D, Yellow Springs Instruments, Yellow Springs, Ohio, USA). HbA1c was measured using a TOSOH HLC-723G7 (Tosoh Corporation, Tokyo, Japan) and ALT using a Roche Modular P and test kits (Roche Diagnostics, Burgess Hill, UK) in a Clinical Pathology Accredited laboratory (Newcastle Upon Tyne Hospital NHS Foundation Trust, Department of Clinical Biochemistry).
Statistical analysis was performed using SPSS V.19 (SPSS, Chicago, USA). Between-group differences were evaluated using a paired t test, and Pearson's correlation was used to investigate associations between variables. Multivariate analyses were undertaken to control for BMI and age with respect to NAFLD. Statistical significance was set at a conservative threshold of p<0.01 to allow for multiple comparisons. Data are mean±SD.
Subjects and Methods
Thirty-seven adults with clinically defined NAFLD were recruited to the study from the subspecialty NAFLD clinic at the Freeman Hospital, Newcastle upon Tyne, UK. These were unrelated patients with NAFLD, derived from a patient population originally identified as having ultrasonographically detected bright liver and abnormal liver biochemical tests. Alternate diagnoses were excluded, including increased alcohol intake (males and females consuming greater than 21/14 units of alcohol per week (>30/20 g/day ethanol), respectively), chronic viral hepatitis (hepatitis B and hepatitis C), autoimmune liver diseases, hereditary haemochromatosis, α1-antitrypsin deficiency, Wilson's disease and drug-induced liver disease. NAFLD was defined as >5% liver fat content on H-MRS. Further exclusion criteria included: implanted ferrous metal; insulin sensitising treatment or dietary change (for people with T2DM, diet and metformin, were acceptable for inclusion if stable for 6 months). Patients had no physical restriction for exercise determined by an exercise test. The control group were individually matched to patients by age, sex and within 3 BMI from a control sample of over 1000 healthy people screened by questionnaire to be free of any metabolic disease.
The study protocol was approved by County Durham and Tees Valley 2 Research Ethics Committee. All participants provided written informed consent. Visits were undertaken at the Clinical Research Facility, Royal Victoria Infirmary, or the Magnetic Resonance Centre, both in Newcastle upon Tyne, UK.
Liver Fat Measurement
Magnetic resonance studies were performed using a 3.0 Tesla Philips Achieva scanner (Philips Medical Systems, Best, The Netherlands). Liver fat was measured by localised H-MRS (PRESS, TR/TR=3000 ms/35 ms, 3×3×3 cmvoxel, SENSE torso Array). Blinded quantification of the spectra (water and CH2 resonances) was performed using the java-based magnetic resonance user interface (jMRUI V.3.0). Following manual first and second-order phase correction, spectra were analysed using a non-linear least squares algorithm (AMARES). Liver fat was expressed as a percentage of liver volume, corrected for proton density of water and lipid.
Physical Activity
Physical activity and energy expenditure were assessed objectively using a multisensor array (SenseWear Pro3, Bodymedia, Pennsylvania, USA) previously validated in healthy adults. Volunteers were asked to wear the armband on their right upper arm for 7 days. All subjects were instructed to remove the armband only for bathing/showering purposes or any water-based activity. A subject's multisensor array data were acceptable for analysis if overall wear-time was ≥80% of the total time that they had the monitor in situ.
The following matrices of physical activity were derived from the multisensor array as units per day: total energy expenditure (TEE); active energy expenditure (AEE); average metabolic equivalents (MET); duration of physical activity (>3.0 METs); duration of moderate physical activity (3.0–5.9 METs); duration of vigorous activity (6.0–9.0 METs); duration of very vigorous activity (≥9.0 METs); number of steps; and duration of monitor worn.
Sedentary Behaviour
Total sedentary duration was classed as total time spent in activities ≤2.9 METs, excluding sleep. Patterns of sedentary behaviour were assessed by power law analyses of the lengths of sedentary bouts fitted from raw sedentary data, as described in more detail previously. Briefly, the density p(x) of sedentary bouts in a time bin width d(x) was plotted against the bout length x on a logarithmic scale to derive power distribution (equation 1) from the shape of the histogram with respect to their length
The type of sedentary distribution characterised by the exponent α (equation 2), can quantify different sedentary behaviour strategies, with a lower α indicating that subjects accumulate sedentary time with a larger proportion of long sedentary bouts:
From these power distributions, Lorentz curves were calculated where the fraction Wx of the total sedentary time that is accumulated in bouts longer than any sedentary period of length x:
The curves are then plotted as Wx/p(x) pairs for each patient and control. Activity patterns were also assessed by assessing transitions from being inactive to active, and normalised by the length of the recording, termed 'Sedentary to Active Transitions'. These data are presented as a percentage of the activity data per day.
Volunteers completed the validated International Physical Activity Questionnaire (IPAQ) to determine levels of physical activity and sit time after wearing the monitor for 7 days. The IPAQ includes four activity domains: job-related physical activity, transportation, housework (including house maintenance and caring for the family), recreation and leisure time activity. The IPAQ was scored using the guidelines produced by The IPAQ Group (http://www.ipaq.ki.se/scoring.pdf).
Anthropometry
Bodyweight (kg) and height (cm) were measured using an electronic scale and stadiometer, respectively, (SECA, Birmingham, UK).
Glucose Control and Liver Enzymes
In the NAFLD group, a blood sample was taken from a forearm vein following an overnight fast (>8 h). Whole blood glucose was measured immediately (YSI 2300 Stat Plus-D, Yellow Springs Instruments, Yellow Springs, Ohio, USA). HbA1c was measured using a TOSOH HLC-723G7 (Tosoh Corporation, Tokyo, Japan) and ALT using a Roche Modular P and test kits (Roche Diagnostics, Burgess Hill, UK) in a Clinical Pathology Accredited laboratory (Newcastle Upon Tyne Hospital NHS Foundation Trust, Department of Clinical Biochemistry).
Statistical Analysis
Statistical analysis was performed using SPSS V.19 (SPSS, Chicago, USA). Between-group differences were evaluated using a paired t test, and Pearson's correlation was used to investigate associations between variables. Multivariate analyses were undertaken to control for BMI and age with respect to NAFLD. Statistical significance was set at a conservative threshold of p<0.01 to allow for multiple comparisons. Data are mean±SD.