Concurrent Training and Prediabetes Comorbidities: An Analysis of Non-responders Using Clinical Cutoff Points
Phase: NA
Conditions: Metabolic Disturbance
Sponsor: Universidad Santo Tomas

Scientific background: When exercise training is adequately supervised and adherence 
is high, it has a therapeutic effect in individuals with prediabetes or type 2 diabetes 
mellitus (T2DM) (Little et al., 2011;Johansen et al., 2017). Many of the cardiometabolic 
risk factors associated with T2DM can be improved or ameliorated with weight control 
and diet, as well as with regular exercise. Indeed, the beneficial effects of high-intensity 
interval (HIIT) and resistance (RT) training on insulin resistance have been extensively 
studied at the level of skeletal muscle and adipose tissue (Zanuso et al., 2017). 
Notwithstanding the irrefutable clinical benefits of exercise in general, there is known to 
be individual variability in the response to different exercise interventions (Bouchard et 
al., 2012b), including CT (Stephens et al., 2015). Whereas some individuals, the so-called 
‘responders’ improve their metabolic profile after a training intervention (e.g., a decrease 
in fasting glucose) others, the ‘non-responders’ show no response or even an opposite 
response (Boulé et al., 2005). From a statistical viewpoint, a non-response for a given 
variable is a lack of difference between a control and a treatment condition. The non-
responder phenomenon has been shown in a non-negligible proportion of participants in 
exercise intervention studies, for instance, in 4–9% of adults at risk for T2DM (Phillips 
et al., 2017) or in 21% of patients with T2DM (Stephens et al., 2015).  Objectives: To 
investigate the effects of a 20-week CT intervention on cardiovascular risk factors such 
as body composition, blood pressure and lipid profile among adult women with 
hyperglycaemia. A secondary aim was to report prevalence of non-responders for the 
different study outcomes. Design: An experimental, and randomized control clinical 
study. Methods: Physically inactive overweight/obese and hyperglycaemic adult women 
between 20 and 50 years, will be randomly assigned to a 20-weeks of an exercise 
intervention group or to a control (non-exercise) group. Comorbidities indices for body 
composition [body mass, waist circumference (WC), fat and lean mass], blood pressure 
and metabolic profile (total, LDL- and HDL-cholesterol, triglycerides, and fasting 
glucose) will be assessed before and after the 20-week intervention. The clinical trials 
will be taken in the Family Healthcare Center Tomás Rojas of the city of Los Lagos, 
Chile, which is a public healthcare centre of this country. We hope to apply the following 
eligibility criteria a) age between 30 and 59 years, b) not living in a rural area, c) physical 
activity levels [as assessed by the International Physical Activity Questionnaire (IPAQ) 
< 600 metabolic equivalents (MET)·min/week, d) non-involvement in regular exercise 
during the previous 6 months, e) fasting glucose of 100–125 mg/dL, and f) 1+ values 
above (or below for HDL-cholesterol) normal cutoffs for: body composition [i.e., WC > 

80 cm, which denotes ‘high cardiovascular risk’ in South American individuals (Alberti 
et al., 2009)]; blood pressure [Systolic (SBP) and diastolic blood pressure (DBP) of 130–
139 mmHg and 85–90 mmHg, respectively, denoting ‘high blood pressure’, or SBP and 
DBP >140 mmHg and 90 mmHg, respectively, denoting ‘hypertension’]; or blood lipid 
profile (i.e., total cholesterol > 200 mg/dL, LDL-cholesterol > 140 mg/dL, HDL-
cholesterol < 50 mg/dL, or triglycerides ≥ 150 mg/dL). Exclusion criteria were: a) 
cardiovascular contraindications to exercise, b) history of stroke, asthma or chronic 
obstructive pulmonary disease, c) muscle-skeletal disorders, and d) smoking. A 
compliance rate to the exercise program ≥ 70% was required for the participants in the 
intervention group to be included in the statistical analyses. 
 
REFERENCES 
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Gibala, M.J. (2011). Low-volume high-intensity interval training reduces hyperglycemia and 
increases muscle mitochondrial capacity in patients with type 2 diabetes. Journal of Applied 
Physiology 111, 1554-1560. 
Johansen, M., Macdonald, C., Hansen, K., and Et Al. (2017). Effect of an intensive lifestyle intervention 
on glycemic control in patients with type 2 diabetes: A randomized clinical trial. JAMA 318, 637-
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Zanuso, S., Sacchetti, M., Sundberg, C.J., Orlando, G., Benvenuti, P., and Balducci, S. (2017). Exercise in 
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Statistical Analysis Plan (SAP): We will apply 2 main forms of statistical analyses a) to 
report the pre-post changes in mean terms, and b) to report the results according with the 
inter-individual responses based on the technical error criteria, where according with the 
error calculated in the three previous measurements registered by the sample that was 
voluntary. Thus, for example, we will classify the subjects in responders and non-
responders by the technical error of measurement, and additionally, we will classify the 
subjects according with a responder and non-responder clinical criteria, where we will 
classify as responders to all subjects who can be able of changing an initial adverse 
clinical altered profile for a healthy new classification. Thus, the specific statistical 
methods that we hope to apply for each analysis are; test of normality and 
homoscedasticity assumptions using Shapiro-Wilk and Levene’s tests, the Student’s t test 
for the identification of differences at baseline. An ANCOVA will be conducted (for 
potential confounders outcomes) in those altered baseline differences outcomes. We also 
hope to apply the repeated measures of two-way (group, time) to assess occurrence of an 
actual training effect [i.e., p <0.05 for the interaction (group × time) for the different study 
outcomes]. Among the specific statistical methods we hope to apply the Bonferroni post 
hoc test we hope to apply when we can be seen test differences among groups. Similarly, 
we hope to apply the Cohen’s d test in order to detect effect size, using the threshold 
values of 0.20, 0.60, 1.2, and 2.0 for small, moderate, large, and very large effects, 
respectively (Hopkins et al., 2009), with 95% confidence intervals (CI). The alpha level 
was fixed at P<0.05 for statistical significance.