Abstract
Background
Diabetes Mellitus (DM) is a fast-growing health problem that imposes an enormous economic burden. Several studies demonstrated the association between physical inactivity and predicting the incidence of diabetes. However, these prediction models have limited validation locally. Therefore, we aim to explore the predictive value of exercise capacity in the incidence of diabetes within a high diabetes prevalence population.
Methodology
A retrospective cohort study including consecutive patients free of diabetes who underwent clinically indicated treadmill stress testing. Diabetic patients at baseline or patients younger than 18 years of age were excluded. Incident diabetes was defined as an established clinical diagnosis post-exercise testing date. The predictive value of exercise capacity was examined using Harrell’s c-index, net reclassification index (NRI), and integrated discrimination index (IDI).
Results
A total of 8,722 participants (mean age 46 ± 12 years, 66.3% were men) were free of diabetes at baseline. Over a median follow-up period of 5.24 (2.17–8.78) years, there were 2,280 (≈ 26%) new cases of diabetes. In a multivariate model adjusted for conventional risk factors, we found a 12% reduction in the risk of incident diabetes for each METs achieved (HR, 0.9; 95% CI, 0.88–0.92; P < 0.001). Using Cox regression, exercise capacity improved the prediction ability beyond the conventional risk factors (AUC = 0.62 to 0.66 and c-index = 0.62 to 0.68).
Conclusion
Exercise capacity improved the overall predictability of diabetes. Patients with reduced exercise capacity are at high risk for developing incidence diabetes. Improvement of both physical activity and functional capacity represents a preventive measure for the general population.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-022-01174-5.
Keywords: Incident Diabetes, Exercise Capacity, Metabolic equivalent of tasks (METs), Net reclassification index, Predictive modeling
Background
Diabetes is a major socio-economic health problem, with prevalence expanding globally over the past decades[1] making diabetes prevention a crucial health priority[2, 3]. Strong evidence engaging higher levels of exercise capacity and lifestyle interventions has shown a protective impact on subsequent diabetes[3, 4]. Physical activity influences cardiorespiratory fitness (CRF), which reflects the well-being of the cardiovascular system and muscular strength[5]. The significance of this evidence has led several organizations, including the American Heart Association, the American Diabetes Association, and the US. Department of Health and Human Services to include exercise capacity in their recommendations and guidelines[6, 7]. However, it remains controversial how other diabetes-related risk factors, especially metabolic syndrome elements, might alter the association between fitness and incident diabetes.
The actual effect of exercise capacity on diabetes has not been fully explored as most previous data exemplify nations with low to intermediate disease prevalence[8, 9]. These data reflect societies where the culture of exercise is dominant; however, in the Middle East and North Africa (MENA), a rise in prevalence is likely to occur considering low activity levels among citizens.
We are aiming to extend the exploration of the predictive value of exercise capacity in incidence diabetes by (1) examining the relationship between exercise capacity and incident diabetes among patients free of diabetes at baseline and (2) examining whether the relationship between CRF and incident diabetes differed across demographic characteristics and other traditional diabetes-related risk factors. Furthermore, we assess the relationship between Duke Treadmill Score, exercise capacity, and incidence of diabetes.
Methods
Study design and population
A retrospective cohort study included patients who underwent clinically indicated exercise treadmill stress testing at King Abdulaziz cardiac center between April 2001 and December 2016. King Abdulaziz Cardiac Center is a part of a large well-connected health care system providing medical service through primary health care up to the tertiary level which makes patients tracking accessible [10]. The patient’s demographics, medical history, and medications used before the stress test were obtained by reviewing the electronic medical record and database search using the ICD-9 coding system. The exercise stress testing system (MUSE) was used to extract exercise test results. Diabetes at the baseline, young patients (< 18 years), records with incomplete stress testing information, and non-Bruce protocol exercise stress testing were excluded from the study analysis. The study was conducted in full accordance with the protocol and the current revision of the declaration of Helsinki, the Good Clinical Practice. The study was a part of the Saudi CArdioRespiratory Fitness (SCARF) project (Study protocol: RC16/103/R - approved by King Abdullah International Medical Research Center (KAIMRC)).
Exercise treadmill stress testing
Patients underwent symptom-limited maximal treadmill stress testing, which followed the standard Bruce protocol. The test day was pointed as the individual study baseline. Individual results of the initial exercise test were included in the database. Resting both heart rate and blood pressure were measured in the seated position and recorded immediately before each test. Supervised clinicians were following American Heart Association/American College of Cardiology (AHA/ACC) guidelines for terminating the test if the patient had Exercise limiting symptoms: chest pain, shortness of breath, significant arrhythmias, abnormal hemodynamic responses, diagnostic ST-segment changes, other limiting symptoms independent of the achieved heart rate or if the participant was unwilling or unable to continue. Otherwise, patients could reach their peak attainable workload independent of the heart rate achieved. Target heart rate was calculated as 85% of the age-predicted maximal heart rate; the patient’s age was subtracted from a constant value of 220. Metabolic equivalents (METs) were adopted to represent cardiorespiratory fitness status based on the workload derived from the maximal speed and grade achieved during the total treadmill time. METs results were categorized into four groups: <6, 6–9, 10–11, and ≥ 12 METs.
Study definitions for risk factors
The history of hypertension was defined as a prior diagnosis of hypertension or the use of antihypertensive medications at the time of stress testing. Dyslipidemia was defined as the prior diagnosis of any significant lipid abnormality in the medical records or lipid-lowering medication use. On a prior angiogram, patients with obstructive coronary artery disease (CAD), prior myocardial infarction, coronary angioplasty, or coronary artery bypass surgery are considered known coronary artery disease. Prior congestive heart failure was defined as a prior clinical diagnosis of systolic or diastolic heart failure.
Study outcome: Incident Diabetes
Incident diabetes was determined among patients without diabetes at baseline and defined as clinical diagnosis of diabetes in the medical records or clinical problem list, use of anti-hyperglycemic medications including insulin, or had lab results suggestive of diabetes post-exercise testing date without further details on the type of diabetes. Time-to-incident diabetes was based on the time between treadmill testing and the date of the first encounter with a new diabetes diagnosis.
Statistical analysis plan
Study participants were divided into four groups based on their METs (< 6, 6–9, 10–11, and ≥ 12). Categorical variables were presented in frequencies and percentages. Continuous variables were presented based on the normality of distribution as mean ± standard deviation or median and interquartile ranges, as appropriate. The four groups were compared using Chi-square or Fisher exact test for categorical variables. Analysis of Variance (ANOVA) and Kruskal Wallis test was used for continuous variables compersion, as appropriate. Cumulative incidence was presented at 5-, 10-, and 15-yr intervals via a bar graph.
Kaplan-Meier cumulative incident diabetes was computed for different exercise capacity groups, and they were compared using the log-rank test. Cox regression was used to compute hazard ratios (HR) and 95% confidence intervals (CI). A forward selection technique was used to demonstrate independent predictors of incident diabetes. In each forward step, we added a related set of variables to improve our model. Gender, age, and heart rate were included in the baseline model (model 1). Subsequently, cardiovascular risk factors (model 2), Medications used (model3), and exercise stress testing findings (model 4). We computed Harrell’s concordance index (C-index) area under the Curve (AUC) and Akaike information criterion (AIC) to compare the models using this methodology developed for the incidence of diabetes. The selection of variables for entry consideration was based on clinical judgment, results of previous publications, and the expertise of the investigators. We also plotted a restricted cubic spline model to show the shape of the continuous relationship between METs and incident diabetes after adjustment for covariates. Finally, we examined the association between METs and incident diabetes in the subset of participants with a BMI measurement (N = 6,539) (Supplementary Table 1). All Statistical analyses were conducted using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as P ≤ 0.05.
Results
Our study included 8,722 participants without diabetes at baseline. The mean age of study participants was 46 ± 12 years, and 66.5% were men; 33.9% were hypertensive; 28.3% had hyperlipidemia; 5.4% were known to have prior coronary artery disease (CAD), and only a few of them had heart failure (Table 1).
Table 1.
Baseline characteristics of the study cohort
| Total | METs < 6 | METs 6–9 | METs 10–11 | METs ≥ 12 | p-value | |
|---|---|---|---|---|---|---|
| 8722 | 589 (6.7%) | 3077 (35.3%) | 2930 (33.6%) | 2126 (24.4%) | ||
| Age (year) | 45.9 ± 12.06 | 50.9 ± 12.37 | 48.4 ± 11.87 | 45.5 ± 11.81 | 41.3 ± 10.96 | < 0.0001 |
| Gender (Male) | 5798 (66.5%) | 254 (43.1%) | 1410 (45.8%) | 2157 (73.6%) | 1977 (93.0%) | < 0.0001 |
| Height (cm) | 162.2 ± 19.51 | 157.8 ± 18.52 | 159.4 ± 17.97 | 164.2 ± 17.85 | 165.0 ± 23.49 | < 0.0001 |
| Weight (kg) | 79.7 ± 16.55 | 81.7 ± 18.08 | 81.3 ± 17.51 | 79.5 ± 16.52 | 77.2 ± 14.11 | < 0.0001 |
| BMI (kg/m2) | 29.4 ± 6.15 | 32.3 ± 7.22 | 31.2 ± 6.58 | 28.5 ± 5.23 | 26.9 ± 5.10 | < 0.0001 |
| Cardiovascular risk factors | ||||||
| Hypertension | 2955 (33.9%) | 258 (43.8%) | 1154 (37.5%) | 987 (33.7%) | 556 (26.2%) | < 0.0001 |
| Hyperlipidaemia | 2465 (28.3%) | 172 (29.2%) | 880 (28.6%) | 884 (30.2%) | 529 (24.9%) | 0.0005 |
| Known CAD | 474 (5.4%) | 27 (4.6%) | 157 (5.1%) | 184 (6.3%) | 106 (5.0%) | 0.0938 |
| Known CHF | 66 (0.8%) | 4 (0.7%) | 30 (1.0%) | 23 (0.8%) | 9 (0.4%) | 0.1593 |
| Prior CABG | 131 (1.5%) | 10 (1.7%) | 57 (1.9%) | 43 (1.5%) | 21 (1.0%) | 0.0885 |
| Prior MI | 454 (5.2%) | 30 (5.1%) | 151 (4.9%) | 172 (5.9%) | 101 (4.8%) | 0.2524 |
| Prior PCI | 306 (3.5%) | 16 (2.7%) | 92 (3.0%) | 123 (4.2%) | 75 (3.5%) | 0.0538 |
| Smoking | 277 (3.2%) | 34 (5.8%) | 88 (2.9%) | 107 (3.7%) | 48 (2.3%) | < 0.0001 |
| Lung disease | 348 (4.0%) | 37 (6.3%) | 145 (4.7%) | 124 (4.2%) | 42 (2.0%) | < 0.0001 |
| Lab results* | ||||||
| Cholesterol | 4.6 (3.87, 5.30) | 4.4 (3.80, 5.20) | 4.5 (3.84, 5.28) | 4.5 (3.87, 5.33) | 4.6 (3.95, 5.35) | 0.0142 |
| Triglyceride | 1.4 (0.99, 1.86) | 1.4 (1.04, 1.94) | 1.3 (0.98, 1.82) | 1.4 (0.98, 1.89) | 1.4 (1.02, 1.91) | 0.0128 |
| HDL | 1.0 (0.88, 1.22) | 1.0 (0.87, 1.22) | 1.1 (0.90, 1.25) | 1.0 (0.87, 1.22) | 1.0 (0.88, 1.18) | < 0.0001 |
| LDL | 2.8 (2.22, 3.49) | 2.7 (2.10, 3.32) | 2.8 (2.19, 3.43) | 2.8 (2.22, 3.51) | 2.9 (2.29, 3.62) | < 0.0001 |
| Haemoglobin | 143 (129, 154) | 133.0 (121, 144) | 136.0 (123, 148) | 146.0 (132, 156) | 152.0 (142, 160) | < 0.0001 |
| Haematocrit | 0.4 (0.39, 0.46) | 0.4 (0.37, 0.43) | 0.4 (0.37, 0.44) | 0.4 (0.40, 0.46) | 0.4 (0.42, 0.47) | < 0.0001 |
| Calcium | 2.3 (2.25, 2.40) | 2.3 (2.20, 2.35) | 2.3 (2.23, 2.39) | 2.3 (2.25, 2.39) | 2.4 (2.28, 2.42) | < 0.0001 |
| BUN | 4.6 (3.80, 5.80) | 4.5 (3.60, 6.00) | 4.5 (3.60, 5.80) | 4.7 (3.80, 5.80) | 4.8 (4.00, 5.80) | < 0.0001 |
| Creatinine | 75.0 (64.00, 89.00) | 69.5 (60.00, 85.00) | 70.0 (60.00, 84.00) | 75.0 (65.00, 88.00) | 81.0 (72.00, 93.00) | < 0.0001 |
| eGFR | 94.7 (80.39, 109.42) | 92.8 (77.29, 106.46) | 93.7 (78.85, 107.83) | 95.8 (81.84, 110.79) | 94.3 (81.44, 110.09) | 0.0001 |
| hsCRP | 2.5 (0.93, 5.00) | 6.0 (2.78, 11.00) | 3.5 (1.24, 6.16) | 2.7 (0.85, 5.00) | 1.4 (0.78, 2.38) | 0.0001 |
| Vitamin-D | 32.9 (23.00, 48.00) | 29.8 (21.00, 47.00) | 33.0 (22.20, 49.00) | 33.3 (23.15, 49.40) | 32.6 (23.90, 46.00) | 0.0001 |
| Medications | ||||||
| Beta-blockers | 710 (8.1%) | 61 (10.4%) | 268 (8.7%) | 247 (8.4%) | 134 (6.3%) | 0.0017 |
| Calcium channel blockers | 503 (5.8%) | 54 (9.2%) | 202 (6.6%) | 159 (5.4%) | 88 (4.1%) | < 0.0001 |
| ACEI | 512 (5.9%) | 40 (6.8%) | 177 (5.8%) | 191 (6.5%) | 104 (4.9%) | 0.0753 |
| ARB | 988 (11.3%) | 86 (14.6%) | 386 (12.5%) | 341 (11.6%) | 175 (8.2%) | < 0.0001 |
| ACEARB | 1288 (14.8%) | 107 (18.2%) | 489 (15.9%) | 452 (15.4%) | 240 (11.3%) | < 0.0001 |
| Aspirin | 875 (10.0%) | 60 (10.2%) | 309 (10.0%) | 328 (11.2%) | 178 (8.4%) | 0.0123 |
| Digoxin | 48 (0.6%) | 7 (1.2%) | 20 (0.6%) | 12 (0.4%) | 9 (0.4%) | 0.0847 |
| PPI | 744 (8.5%) | 59 (10.0%) | 271 (8.8%) | 284 (9.7%) | 130 (6.1%) | < 0.0001 |
| Plavix | 2057 (23.6%) | 149 (25.3%) | 718 (23.3%) | 717 (24.5%) | 473 (22.2%) | 0.2169 |
| Statin | 1388 (15.9%) | 86 (14.6%) | 483 (15.7%) | 506 (17.3%) | 313 (14.7%) | 0.0674 |
| Diuretic | 357 (4.1%) | 41 (7.0%) | 154 (5.0%) | 110 (3.8%) | 52 (2.4%) | < 0.0001 |
| Stress test results | ||||||
| Rest HR (bpm) | 81.9 ± 16.12 | 86.9 ± 18.45 | 84.8 ± 17.00 | 81.1 ± 15.05 | 77.5 ± 14.21 | < 0.0001 |
| Peak HR (bpm) | 155.5 ± 22.13 | 130.9 ± 25.28 | 149.8 ± 21.76 | 158.5 ± 18.79 | 166.7 ± 17.53 | < 0.0001 |
| Rest SBP (mmHg) | 129.8 ± 16.94 | 133.0 ± 19.06 | 130.7 ± 18.34 | 129.1 ± 16.04 | 128.4 ± 14.98 | < 0.0001 |
| Peak SBP (mmHg) | 160.8 ± 25.92 | 152.7 ± 32.48 | 160.7 ± 28.64 | 162.4 ± 24.55 | 160.7 ± 21.29 | < 0.0001 |
| Rest DBP (mmHg) | 79.2 ± 9.88 | 79.5 ± 10.07 | 78.6 ± 10.38 | 79.6 ± 9.45 | 79.5 ± 9.62 | 0.0034 |
| Peak DBP (mmHg) | 82.8 ± 10.63 | 82.3 ± 10.89 | 83.4 ± 10.77 | 83.4 ± 10.76 | 81.2 ± 10.06 | < 0.0001 |
| Peak METs | 10.1 ± 2.80 | 4.7 ± 0.95 | 8.0 ± 1.04 | 10.7 ± 0.62 | 13.6 ± 1.48 | < 0.0001 |
| Chronotropic Incompetence | 2275 ( 26.1% ) | 391 ( 66.4% ) | 1013 ( 32.9% ) | 591 ( 20.2% ) | 280 ( 13.2% ) | < 0.0001 |
| Duke Treadmill Score | 5.5 ± 6.12 | 1.8 ± 5.79 | 4.0 ± 5.43 | 6.2 ± 5.73 | 7.9 ± 6.56 | < 0.0001 |
| Low | 6040 (69.3%) | 114 (19.4%) | 2064 (67.1%) | 2221 (75.8%) | 1641 (77.2%) | < 0.0001 |
| Moderate | 2447 (28.1%) | 454 (77.1%) | 925 (30.1%) | 597 (20.4%) | 471 (22.2%) | |
| High | 235 (2.7%) | 21 (3.6%) | 88 (2.9% ) | 112 (3.8% ) | 14 (0.7%) |
CAD: Coronary artery disease, CHF: Congestive heart failure, CABG: Coronary artery bypass grafting, MI: Myocardial infarction, PCI: Percutaneous coronary intervention, HDL: High density lipoprotein, LDL: Low density lipoprotein, BUN: Blood urea nitrogen, eGFR: estimated Glomerular filtration rate, ACEI: Angiotensin converting enzyme inhibitors, ARB: Angiotensin Receptor blocker, PPI: Proton pump inhibitors, HR: Heart rate, SBP: Systolic blood pressure, DBP: Diastolic blood pressure, METs: Metabolic equivalent of tasks
Participants with the highest exercise capacity (≥ 12METs) were younger (41 ± 3 vs. 51 ± 12 years, p < 0.001); more often males (91% versus 43%, p < 0.001); had lower mean body mass index (26 ± 9 vs. 32 ± 3 kg/m2, p < 0.001) and less likely to have hypertension or hyperlipidemia (26% vs. 44% and 25% vs. 29%, respectively, p < 0.001) in comparison with the lowest exercise capacity group (METs < 6). No apparent differences between CRF categories regarding prior coronary artery disease (CAD) and heart failure were observed. At peak exercise, the heart rate was higher among the highest achievers (166 ± 2 vs. 131 ± 25 bpm, p < 0.001) as well as systolic blood pressure (161 ± 21 vs. 153 ± 33 mmHg, p < 0.001). Moreover, the highest METs-achieved was associated with a lower risk of Duke Treadmill risk, while high Duke Treadmill risk was observed in those with lower METs achieved (for low Duke score: 77.2% vs. 19.4%) and (for high Duke score 0.7% vs. 2.7%), p < 0.001.
Over a median follow-up period of 5.24 (IQR: 2.17–8.78) years, there were 2,280 (25.96%) new cases of diabetes. The unadjusted 5-year accumulative incidence rates of diabetes across categories of CRF (< 6, 6 < 9, 10 < 11, and ≥ 12 METs) were 25.1%, 16.9%, 9.4%, and 6.2%, respectively. The cumulative rates of incidence of diabetes by the end of 15 year follow-up period were as follows: 40.6%, 31.9%, 23.2%, and 17.3% for those who achieved < 6, 6 < 9, 10 < 11, and ≥ 12 METs, respectively (Fig. 1).
Fig. 1.
The cumulative rates of incidence diabetes
Examination of the crude association between categories of METs and risk for incident diabetes using a Kaplan-Meier cumulative incidence curve revealed a significant trend across categories of METs (log-rank < 0.001) (Fig. 2). In a multivariate Cox regression model adjusted for potential confounders, we found a 10% reduction in the risk of incident diabetes with higher METs achieved (HR, 0.90; 95% CI, 0.88–0.92; p < 0.001) (model 4B) and risk for incident diabetes reduced per each METs achieved; 13% (6 < 9 METs), 38% (10 < 11), and 52% (≥ 12 METs) compare to lower METs (< 6 METs) p < 0.001. Adding the exercise capacity information (METs achieved, CI, and Duke Risk score) improves the overall predictability of the model (model 4 A) over the baseline model (Table 2A). Examining the predictive value of the finding of exercise stress testing (using AIC, C-statistics, IAUC, NRI, and IDI), we found superiority of METs groups in the prediction incidence of diabetes above the other models resulting in a significant reclassification of the study cohort and significant improvement of the area under the curve above the primary model (Table 2B).
Fig. 2.
Kaplan-Meier cumulative incidence curve and the trends across categories of METs
Table 2A.
Adjusted multivariate Cox regression model for exercise stress testing
| Model (1) | Model (2) | Model (3) | Model (4 A) | Model (4B) | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p | HR (95% CI) | p | |
| Age (years) | 1.032 (1.03–1.04) | < 0.001 | 1.028 (1.02–1.03) | < 0.001 | 1.028 (1.02–1.03) | < 0.001 | 1.022 (1.02–1.03) | < 0.001 | 1.02 (1.02–1.02) | < 0.001 |
| Gender (Female vs. Male) | 1.131 (1.03–1.24) | 0.009 | 1.176 (1.07–1.29) | 0.001 | 1.138 (1.04–1.25) | 0.007 | 0.892 (0.81–0.99) | 0.027 | 0.87 (0.79–0.96) | 0.007 |
| Resting heart rate (bpm) | 1.002 (1.00–1.00) | 0.109 | 1.003 (1.00-1.01) | 0.011 | 1.004 (1.00-1.01) | 0.003 | 1.002 (1.00-1.01) | 0.109 | 1.001 (1.00–1.00) | 0.452 |
| Cardiovascular risk factors | ||||||||||
| Hypertension | 1.189 (1.08–1.31) | < 0.001 | 1.138 (1.03–1.26) | 0.015 | 1.06 (0.95–1.18) | 0.284 | 1.059 (0.95–1.18) | 0.293 | ||
| Hyperlipidemia | 1.196 (1.08–1.32) | < 0.001 | 1.502 (1.36–1.66) | < 0.001 | 1.537 (1.39–1.70) | < 0.001 | 1.539 (1.39–1.71) | < 0.001 | ||
| Smoking | 1.202 (0.94–1.54) | 0.150 | 1.241 (0.97–1.59) | 0.090 | 1.19 (0.93–1.52) | 0.167 | 1.178 (0.92–1.51) | 0.193 | ||
| Lung disease | 1.06 (0.77–1.46) | 0.720 | 0.961 (0.69–1.34) | 0.814 | 0.94 (0.67–1.31) | 0.716 | 0.908 (0.65–1.27) | 0.572 | ||
| Known CAD | 1.154 (0.98–1.36) | 0.092 | 1.149 (0.97–1.37) | 0.117 | 1.18 (0.99–1.40) | 0.060 | 1.189 (1.00-1.41) | 0.049 | ||
| Known CHF | 0.588 (0.36–0.97) | 0.039 | 0.483 (0.29–0.81) | 0.005 | 0.431 (0.26–0.72) | 0.001 | 0.429 (0.26–0.71) | 0.001 | ||
| Medications | ||||||||||
| Statins | 0.332 (0.28–0.40) | < 0.001 | 0.338 (0.28–0.41) | < 0.001 | 0.338 (0.28–0.41) | < 0.001 | ||||
| PPI | 1.632 (1.32–2.01) | < 0.001 | 1.563 (1.27–1.93) | < 0.001 | 1.568 (1.27–1.94) | < 0.001 | ||||
| CCB | 0.994 (0.81–1.21) | 0.955 | 1.005 (0.82–1.23) | 0.964 | 0.999 (0.82–1.22) | 0.993 | ||||
| BB | 1.697 (1.38–2.09) | < 0.001 | 1.615 (1.31–1.99) | < 0.001 | 1.588 (1.29–1.96) | < 0.001 | ||||
| ACE/ARB | 0.996 (0.86–1.15) | 0.951 | 0.979 (0.85–1.13) | 0.771 | 0.988 (0.86–1.14) | 0.866 | ||||
| Diuretic | 1.187 (0.95–1.48) | 0.124 | 1.182 (0.95–1.47) | 0.138 | 1.164 (0.93–1.45) | 0.178 | ||||
| METs (Categories) | ||||||||||
| METs 6–9 | 0.871 (0.75–1.01) | 0.073 | ||||||||
| METs 10–11 | 0.619 (0.52–0.73) | < 0.001 | ||||||||
| METs ≥ 12 | 0.482 (0.39–0.59) | < 0.001 | ||||||||
| METs (Continuous) | 0.90 (0.88–0.92) | < 0.001 | ||||||||
| Chronotropic incompetence | 1.119 (1.01–1.24) | 0.030 | 1.056 (0.95–1.17) | 0.303 | ||||||
| Duke Risk Score | ||||||||||
| High Risk | 1.293 (1.00-1.67) | 0.048 | 1.257 (0.97–1.62) | 0.078 | ||||||
| Moderate Risk | 1.26 (1.14–1.39) | < 0.001 | 1.21 (1.10–1.33) | < 0.001 | ||||||
h: Hazard ratio, CI: confidence interval, CAD: Coronary artery disease, CHF: Congestive heart failure, PPI: Proton pump inhibitors, CCB: Calcium channel blocker, BB: beta-blockers, ACE/ARB: Angiotensin-converting enzyme/Angiotensin receptor blockers, METs: Metabolic equivalent of tasks,
Table 2B.
Predictive value of the models finding for exercise stress testing
| Model (1) | Model (2) | Model (3) | Model (4 A) | Model (4B) | |
|---|---|---|---|---|---|
| AIC | 36619.287 | 36573.79 | 36408.081 | 36253.129 | 36227.135 |
| C-Statistics | 0.6221 | 0.6289 | 0.6581 | 0.6817 | 0.6839 |
| IAUC | 0.6256 | 0.6352 | 0.6581 | 0.6911 | 0.6948 |
| NRI | 0.3757 | 0.2451 | 0.3562 | 0.1595 | 0.129 |
| IDI | 0.0123 | 0.0019 | 0.0065 | 0.0026 | 0.0026 |
Model [1]: Age, Gender, Resting heart rate.
Model [2]: Model [1] + Cardiovascular risk factors.
Model (3): Model (2) + Medications used.
Model (4A): Model (3) + Finding of exercise stress testing (METs in categories)
Model (4B): Model (3) + Finding of exercise stress testing (METs as continuous)
AIC: Akaike information criterion, C-statistics: concordance statistic, IAUC: Incremental Area Under the Curve, NRI: Net reclassification improvement, IDI: Integrated Discrimination Index
A non-linear inverse relationship was observed between baseline exercise capacity and risk of incident diabetes after adjusting for known confounders. A gradual decline in the risk of development of diabetes was observed for every improvement of exercise capacity (METs increase) above 6 METs (Fig. 3).
Fig. 3.
The relationship of incidence diabetes risk observed for every improvement of exercise capacity
Discussion
This study investigated the relationship between exercise capacity and incident diabetes. To the best of our knowledge, it is the first large and demographically diverse cohort to examine the association between CRF estimated by METs using a standard test (Bruce protocol) by exercise treadmill testing and the risk of incident diabetes in the MENA region. It has shown that higher fitness was inversely associated with incident diabetes, such that every increment of fitness by 1 METs was associated with a 12% lower risk of incident diabetes. Another important finding in our study was the inverse linear relationship between CRF and diabetes, which notably did not show evidence of plateauing or reversal at higher levels of CRF regardless of age, sex, or other diabetes risk factors.
The mechanism that links the relationship between fitness and incident diabetes is a subject of much discussion. Individuals with low cardiorespiratory fitness have high insulin resistance[11], and low levels of CRF have fewer glucose transporters[12] compared with those more fit. It is thought to be mediated by positive changes in the human body tissue profile, reduced adiposity[13], and an immediate increase in insulin sensitivity and glucose disposal[14]. However, several studies have shown that impaired oxidative respiration due to mitochondrial dysfunction[15] underlies the pathogenesis of diabetes reflecting a native genetic state independent of physical activity[16].
Scattered small cross-sectional studies have shown that exercise capacity is inversely associated with impaired glycemic control[17], the metabolic syndrome[18], diabetes[19] and positively associated with glucose disposal rate and insulin sensitivity[20]. Unfortunately, these studies have been small and confined to limited demographic settings (single-sex, racial group, or age-group). Others had reported the relationship between CRF level and the incident diabetes in prospective[21–23] or measuring CFR differently[24].
Low CFR in diabetic patients is an independent risk factor for mortality, and it was associated with 2.1 odds of mortality compared with high CFR diabetic patients[25]. Long-standing diabetes leads to consequences of peripheral neuropathy and vasculopathy with the reduction in the activity of daily living, poor physical function, and disability[26–28]. Additionally, concomitant diabetes risk factors expedite the complications’ progression and highlight the need for urgent intervention on several prevention levels[29]. Implementing the WHO recommendations for reducing physical inactivity by 10–15% will gradually reduce T2DM in adults[30]. A 150-min of moderate-intensity exercise weekly was associated with a reduction of T2DM by 26%[31].
Study limitations and strengths
Our study is not without limitations. First, cardiovascular health is independently associated with behaviors that may represent the causal contributing to fitness. Unfortunately, we are unable to assess these behaviors in our study formally. Second, incident diabetes was based on medical records and administrative claims files, which were not collected initially to examine diabetes. As a result, our study did not include study protocol–based direct measurements of hemoglobin A1c, blood glucose, or oral glucose tolerance testing. Because of this, several people with undiagnosed diabetes may have been included in our study population or missed as incident cases, attenuating our results. Third, our study population comprised persons referred for stress testing, which undoubtedly carries a higher burden of cardiovascular disease at baseline than the general population and may have led to referral bias. That limits our study’s generalizability. Fourth, residual confounding is always a concern with observational studies, especially with covariates assessed via the medical record rather than a direct measurement. Finally, our study reports a single center’s experience with its unique practice patterns and mode of operation. Thus, although the cohort studied was diverse, it may not represent the entire adult population of Saudi Arabia.
On the other hand, our study has some strengths. The standard stress testing, the Bruce protocol treadmill test, characterized fitness rather than self-reporting physical activity. This way is readily interpreted in clinical settings. Furthermore, our study sample was a large and diverse sample, which adequately powered our study.
Conclusion
In conclusion, our results showed a strong inverse relationship between cardiorespiratory fitness and the development of type 2 diabetes. This relationship was independent of other traditional risk factors for diabetes. This study provides further supporting evidence of the benefits of higher overall fitness. Maintaining a high cardiorespiratory fitness level may contribute to the prevention of type 2 diabetes; therefore, we hope our findings will encourage health professionals to advise the general public and diabetics to lead an active lifestyle and improve their fitness levels. Prospective future studies are required to evaluate these findings further.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
None.
Abbreviations
- DM
Diabetes mellitus
- CRF
Cardiorespiratory fitness
- MENA
Middle East and North Africa
- ICD
International Classification of Diseases
- SCARF project
Saudi CArdioRespiratory Fitness project
- KAIMRC
King Abdulla International Medical Research Center
- AHA/ACC
American Heart Association/American College of Cardiology
- METs
Metabolic equivalents of tasks
- CAD
Coronary artery disease
- ANOVA
Analysis of Variance
- HR
Hazard ratios
- CI
confidence intervals
- AIC
Akaike information criterion
- AUC
Area under the Curve
- C-index
Concordance index
- IDI
Integrated discrimination index
- AIC
Akaike information criterion
- C-statistics
concordance statistic
- IAUC
Incremental Area Under the Curve
- NRI
Net reclassification improvement
- IDI
Integrated Discrimination Index
Authors’ contributions
All authors (AJ, HA, AA, and MA) contributed equally to all stages of the study and agree on the final version of the manuscript.
Funding
The study has no funding apart from publication fee.
Data availability
The datasets generated are not for publicly sharing according to the policy declared by King Abdullah International Medical Research Center in Riyadh-Saudi Arabia and a data sharing agreement has to be signed for that with King Abdullah International Medical Research Center through the corresponding author on reasonable request.
Declarations
Ethics approval and informed consent to participate
The study protocol and waved consent were approved by ethical committee of King Abdullah International Medical Research Center. Study protocol number : RC16/103/R.
Consent for publication
Not applicable.
Competing interests
The authors declare that there are no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Abdelrahman A. Jamiel, Email: abdujamiel@yahoo.com
Amjad M. Ahmed, Email: amjadhabib87@hotmail.com
References
- 1.Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513–30. doi: 10.1016/S0140-6736(16)00618-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Torjesen I. NHS England rolls out world’s first national diabetes prevention programme. BMJ. 2016;352:i1669. doi: 10.1136/bmj.i1669. [DOI] [PubMed] [Google Scholar]
- 3.Aune D, Norat T, Leitzmann M, Tonstad S, Vatten LJ. Physical activity and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Epidemiol. 2015;30(7):529–42. doi: 10.1007/s10654-015-0056-z. [DOI] [PubMed] [Google Scholar]
- 4.Lin X, Zhang X, Guo J, Roberts CK, McKenzie S, Wu WC, et al. Effects of Exercise Training on Cardiorespiratory Fitness and Biomarkers of Cardiometabolic Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Am Heart Assoc. 2015;4(7). [DOI] [PMC free article] [PubMed]
- 5.Colberg SR, Albright AL, Blissmer BJ, Braun B, Chasan-Taber L, Fernhall B, et al. Exercise and type 2 diabetes: American College of Sports Medicine and the American Diabetes Association: joint position statement. Exercise and type 2 diabetes. Med Sci Sports Exerc. 2010;42(12):2282–303. doi: 10.1249/MSS.0b013e3181eeb61c. [DOI] [PubMed] [Google Scholar]
- 6.Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, et al. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019;74(10):1376–414. doi: 10.1016/j.jacc.2019.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Jarvie JL, Pandey A, Ayers CR, McGavock JM, Sénéchal M, Berry JD, et al. Aerobic Fitness and Adherence to Guideline-Recommended Minimum Physical Activity Among Ambulatory Patients With Type 2 Diabetes Mellitus. Diabetes Care. 2019;42(7):1333–9. doi: 10.2337/dc18-2634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Xu C, Hou Y, Si K, Cao Z. Cardiorespiratory fitness, genetic susceptibility, inflammation and risk of incident type 2 diabetes: A population-based longitudinal study. Metabolism. 2022;132:155215. doi: 10.1016/j.metabol.2022.155215. [DOI] [PubMed] [Google Scholar]
- 9.Zaccardi F, O’Donovan G, Webb DR, Yates T, Kurl S, Khunti K, et al. Cardiorespiratory fitness and risk of type 2 diabetes mellitus: A 23-year cohort study and a meta-analysis of prospective studies. Atherosclerosis. 2015;243(1):131–7. doi: 10.1016/j.atherosclerosis.2015.09.016. [DOI] [PubMed] [Google Scholar]
- 10.MNGHA. About the Ministry of National Guard Health Affairs (MNGHA). 2022. Available from: https://ngha.med.sa/english/aboutngha/pages/default.aspx.
- 11.Sabag A, Keating SE, Way KL, Sultana RN, Lanting SM, Twigg SM, et al. The association between cardiorespiratory fitness, liver fat and insulin resistance in adults with or without type 2 diabetes: a cross-sectional analysis. BMC Sports Sci Med Rehabil. 2021;13(1):40. doi: 10.1186/s13102-021-00261-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Farooq M, Jorde UP. Impact of Sodium-Glucose Co-Transporter-2 Inhibitors (SGLT2i) On Cardiac Bioenergetic Properties and Cardiorespiratory Fitness: A Special Effect of SGLT2i In Heart Failure? Cardiol Rev. 2021. [DOI] [PubMed]
- 13.Wong SL, Katzmarzyk P, Nichaman MZ, Church TS, Blair SN, Ross R. Cardiorespiratory fitness is associated with lower abdominal fat independent of body mass index. Med Sci Sports Exerc. 2004;36(2):286–91. doi: 10.1249/01.MSS.0000113665.40775.35. [DOI] [PubMed] [Google Scholar]
- 14.Agostinis-Sobrinho C, Rosário R, Santos R, Norkiene S, Mota J, Rauckienė-Michaelsson A, et al. Higher Cardiorespiratory Fitness Levels May Attenuate the Detrimental Association between Weight Status, Metabolic Phenotype and C-Reactive Protein in Adolescents-A Multi-Cohort Study. Nutrients. 2020;12(5). [DOI] [PMC free article] [PubMed]
- 15.Abushamat LA, McClatchey PM, Scalzo RL, Schauer I, Huebschmann AG, Nadeau KJ, et al. Mechanistic Causes of Reduced Cardiorespiratory Fitness in Type 2 Diabetes. J Endocr Soc. 2020;4(7):bvaa063. doi: 10.1210/jendso/bvaa063. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ferrer J, Aoki M, Behn P, Nestorowicz A, Riggs A, Permutt MA. Mitochondrial glycerol-3-phosphate dehydrogenase. Cloning of an alternatively spliced human islet-cell cDNA, tissue distribution, physical mapping, and identification of a polymorphic genetic marker. Diabetes. 1996;45(2):262–6. doi: 10.2337/diab.45.2.262. [DOI] [PubMed] [Google Scholar]
- 17.Gulsin GS, Henson J, Brady EM, Sargeant JA, Wilmot EG, Athithan L, et al. Cardiovascular Determinants of Aerobic Exercise Capacity in Adults With Type 2 Diabetes. Diabetes Care. 2020;43(9):2248–56. doi: 10.2337/dc20-0706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Finley CE, LaMonte MJ, Waslien CI, Barlow CE, Blair SN, Nichaman MZ. Cardiorespiratory fitness, macronutrient intake, and the metabolic syndrome: the Aerobics Center Longitudinal Study. J Am Diet Assoc. 2006;106(5):673–9. doi: 10.1016/j.jada.2006.02.012. [DOI] [PubMed] [Google Scholar]
- 19.Williams PT. Vigorous exercise, fitness and incident hypertension, high cholesterol, and diabetes. Med Sci Sports Exerc. 2008;40(6):998–1006. doi: 10.1249/MSS.0b013e31816722a9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Larsen FJ, Anderson M, Ekblom B, Nyström T. Cardiorespiratory fitness predicts insulin action and secretion in healthy individuals. Metabolism. 2012;61(1):12–6. doi: 10.1016/j.metabol.2011.05.010. [DOI] [PubMed] [Google Scholar]
- 21.Sloan RA, Sawada SS, Gando LIM, Kawakami Y, Okamoto R, et al. The Association of Fit-Fat Index with Incident Diabetes in Japanese Men: A Prospective Cohort Study. Sci Rep. 2018;8(1):569. doi: 10.1038/s41598-017-18898-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Katzmarzyk PT, Craig CL, Gauvin L. Adiposity, physical fitness and incident diabetes: the physical activity longitudinal study. Diabetologia. 2007;50(3):538–44. doi: 10.1007/s00125-006-0554-3. [DOI] [PubMed] [Google Scholar]
- 23.Sloan RA, Haaland BA, Sawada SS, Lee IM, Sui X, Lee DC, et al. A Fit-Fat Index for Predicting Incident Diabetes in Apparently Healthy Men: A Prospective Cohort Study. PLoS ONE. 2016;11(6):e0157703. doi: 10.1371/journal.pone.0157703. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Holtermann A, Gyntelberg F, Bauman A, Jensen MT. Cardiorespiratory fitness, fatness and incident diabetes. Diabetes Res Clin Pract. 2017;134:113–20. doi: 10.1016/j.diabres.2017.10.001. [DOI] [PubMed] [Google Scholar]
- 25.Wei M, Gibbons LW, Kampert JB, Nichaman MZ, Blair SN. Low cardiorespiratory fitness and physical inactivity as predictors of mortality in men with type 2 diabetes. Ann Intern Med. 2000;132(8):605–11. doi: 10.7326/0003-4819-132-8-200004180-00002. [DOI] [PubMed] [Google Scholar]
- 26.Moreira Bde S, Sampaio RF, Furtado SR, Dias RC, Kirkwood RN. The Relationship Between Diabetes Mellitus, Geriatric Syndromes, Physical Function, and Gait: A Review of the Literature. Curr Diabetes Rev. 2016;12(3):240–51. doi: 10.2174/1573399811666150615142746. [DOI] [PubMed] [Google Scholar]
- 27.de Carvalho e Silva F, Jakimiu FO, Skare TL. Diabetic hands: a study on strength and function. Diabetes Metab Syndr. 2014;8(3):162–5. doi: 10.1016/j.dsx.2014.04.020. [DOI] [PubMed] [Google Scholar]
- 28.Oh TJ, Kang S, Lee JE, Moon JH, Choi SH, Lim S, et al. Association between deterioration in muscle strength and peripheral neuropathy in people with diabetes. J Diabetes Complications. 2019;33(8):598–601. doi: 10.1016/j.jdiacomp.2019.04.007. [DOI] [PubMed] [Google Scholar]
- 29.Muntner P, Whelton PK, Woodward M, Carey RM. A Comparison of the 2017 American College of Cardiology/American Heart Association Blood Pressure Guideline and the 2017 American Diabetes Association Diabetes and Hypertension Position Statement for U.S. Adults With Diabetes. Diabetes Care. 2018;41(11):2322–9. doi: 10.2337/dc18-1307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Medina C, Coxson P, Penko J, Janssen I, Bautista-Arredondo S, Barquera S, et al. Cardiovascular and diabetes burden attributable to physical inactivity in Mexico. Cardiovasc Diabetol. 2020;19(1):99. doi: 10.1186/s12933-020-01050-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Wahid A, Manek N, Nichols M, Kelly P, Foster C, Webster P, et al. Quantifying the Association Between Physical Activity and Cardiovascular Disease and Diabetes: A Systematic Review and Meta-Analysis. J Am Heart Assoc. 2016;5(9). [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated are not for publicly sharing according to the policy declared by King Abdullah International Medical Research Center in Riyadh-Saudi Arabia and a data sharing agreement has to be signed for that with King Abdullah International Medical Research Center through the corresponding author on reasonable request.