Percentage of Age-Predicted Cardiorespiratory Fitness May Be a Stronger Risk Indicator for Incident Type 2 Diabetes Than Absolute Levels of Cardiorespiratory Fitness : Journal of Cardiopulmonary Rehabilitation and Prevention

Secondary Logo

Journal Logo


Percentage of Age-Predicted Cardiorespiratory Fitness May Be a Stronger Risk Indicator for Incident Type 2 Diabetes Than Absolute Levels of Cardiorespiratory Fitness

Kunutsor, Setor K. PhD; Khan, Hassan PhD; Seidu, Samuel MD; Laukkanen, Jari A. PhD

Author Information
Journal of Cardiopulmonary Rehabilitation and Prevention 43(1):p 66-73, January 2023. | DOI: 10.1097/HCR.0000000000000720

Diabetes, of which type 2 diabetes (T2D) accounts for >90% of cases, is a leading cause of morbidity, mortality, and associated with substantial economic burden on health care and global systems.1,2 Major risk factors for T2D constitute older age, non-White ethnicity, obesity, family history of T2D, poor diet, smoking, excessive alcohol consumption, and physical inactivity.3 Despite increasing knowledge on the epidemiology of T2D and development of successful preventive strategies, its prevalence and incidence are not on the decrease. There is documented evidence on the role of regular physical activity (PA) in reducing the risk of adverse cardiometabolic outcomes including T2D.4,5 Cardiorespiratory fitness (CRF), a cardiopulmonary exercise testing (CPX) parameter and one of the best measures for assessing cardiovascular fitness and aerobic capacity, can be increased through increased PA and exercise training.6 A wide variation of methods are used to assess CRF and these range from directly measured maximal oxygen uptake (V˙o2max) or peak oxygen uptake (V˙o2peak) during CPX,6,7 to estimation from exercise tests and nonexercise prediction equations. Like PA, several observational studies have shown that CRF, either directly measured or estimated from exercise tests, is inversely and independently associated with cardiovascular outcomes8–13 as well as T2D.14 In addition to the fact that about half of the variation in CRF is heritable,15 CRF is strongly influenced by age16; hence, CRF can be expressed as a percentage of the value predicted on the basis of age (percentage of age-predicted CRF, %age-predicted CRF). Some observational prospective studies have shown that %age-predicted CRF is inversely associated with cardiovascular outcomes such as cardiovascular disease (CVD), coronary heart disease, sudden cardiac death, and hypertension as well as mortality.6,17–21 However, the association between %age-predicted CRF and T2D, a major risk factor for vascular disease, has not been previously investigated. The primary aim of this study was to evaluate the nature, magnitude, and specificity of the association between %age-predicted CRF and risk of incident T2D in a population-based cohort of apparently healthy middle-aged and older men from eastern Finland. To put the strength of the association of %age-predicted CRF with the risk of future T2D into context, a comparison was made with the association between absolute CRF levels and T2D risk in the same sample of study participants.


The current study adhered to STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines for reporting observational studies in epidemiology (see Supplemental Digital Content 1, available at: We employed data from the Kuopio Ischaemic Heart Disease (KIHD) risk factor study, a population-based prospective cohort study that was designed to investigate established and emerging risk factors for atherosclerotic cardiovascular outcomes in eastern Finland. Participants included in the KIHD study comprised a representative sample of men aged 42-60 yr living in the city of Kuopio and its surrounding rural communities in eastern Finland. Invitations were sent to potential study participants during the recruitment phase. Of the representative sample of 3433 potentially eligible men who were invited for screening examinations between March 1984 and December 1989, 3235 were found to be eligible. Of this number, 2682 (83%) volunteered to participate and 553 did not respond to the invitation or declined to give informed consent. For this analysis, men with a prevalent history of T2D (n = 112) and those with missing data on the exposure and potential confounders (n = 669) were excluded. The current analysis included 1901 men with complete information on %age-predicted CRF, absolute CRF, relevant risk markers, and incident T2D events (see Supplemental Digital Content 2, available at: The Research Ethics Committee of the University of Eastern Kuopio, Finland, approved the study, which was conducted in accordance with the Declaration of Helsinki. Each participant provided written informed consent.


Peak oxygen uptake, used as a measure of CRF, was directly assessed using a computerized metabolic measurement system (Medical Graphics) during a maximal symptom-limited CPX on an electrically braked cycle ergometer, which was conducted between 8:00-10:00 am.23 Repeat measurements of CRF were performed 11 yr after baseline in a random subset of the study participants.12,24,25 The standardized testing protocol included a 3-min warm-up at 50 W, followed by 20-W/min increases in workload with direct analyses of expired respiratory gases. The respiratory gas analyzer expressed V˙o2peak as an average value recorded over 8 sec. The V˙o2peak was defined as the highest or peak attained value for V˙o2, expressed as mL/kg/min; V˙o2peak was also expressed in metabolic equivalents (METs) (1 MET corresponding to an V˙o2 of 3.5 mL/kg/min). The respiratory exchange ratio (RER), defined as the ratio between respiratory gases (V˙co2 and V˙o2), was obtained exclusively from ventilatory expired gas analysis. Maximal effort was defined as RER ≥ 1.1.16o22peak expressed in METs was incorporated into a regression equation [18.4 − (0.15 × age)], which was used to estimate age-predicted CRF.26 The %age-predicted CRF was then calculated from the formula: (achieved CRF/age-predicted CRF) × 100.21,26 Given the strong influence of age on CRF and the need to assess if %age-predicted CRF was a stronger risk indicator than absolute levels of CRF, the regression equation for age by Morris and colleagues26 was the most appropriate equation to use rather than equations that took into account multiple factors.27 Furthermore, we used this approach to maintain consistency with previous reports.18–21 An experienced physician and nurse supervised all exercise tests to ensure safety. Electrocardiographic indices, blood pressure, and heart rate were measured during CPX.

The assessment of lifestyle characteristics and medical history, collection of blood specimens and measurement of blood biomarkers, and physical examinations have been described in previous reports.28,29 Briefly, self-administered questionnaires were used to assess sociodemographic characteristics, lifestyle characteristics such as smoking and alcohol consumption, existing medical conditions, and use of medication.28 Prevalent T2D was defined as a fasting blood glucose level ≥7.0 mmol/L or clinical diagnosis of diabetes with dietary, oral, or insulin treatment. The cholesterol content of lipoprotein fractions was measured enzymatically (Boehringer Mannheim).30 Adulthood socioeconomic status (SES) was assessed as a summary index of the combination of income, education, occupation, occupational prestige, material standard of living, and housing conditions. The composite SES index ranged from 0-25, with higher values indicating lower SES.31 Smoking status was categorized into smokers and nonsmokers. A participant was defined as a smoker if he had ever smoked regularly and had smoked cigarettes, cigars, or a pipe within the past 30 d. Alcohol consumption reported in g/wk was assessed using the Nordic Alcohol Consumption Inventory. Resting blood pressure was measured between 8:00-10:00 am with a random-zero sphygmomanometer using a standardized protocol. After a supine rest of 5 min, blood pressure was measured three times in supine position, once in standing position, and twice in sitting position with 5-min intervals, and the arithmetic mean of all available measurements was taken. Leisure-time PA was assessed from a 12-mo PA history questionnaire modified from the Minnesota Leisure-Time Physical Activity Questionnaire.32

We included all T2D cases that occurred from study enrollment through to 2018. An incident T2D case was defined as a fasting plasma glucose (FPG) ≥ 7.0 mmol/L, a 2-h glucose tolerance test plasma glucose ≥ 11.1 mmol/L, or use of glucose-lowering medication according to self-report at reexamination and by record linkage to the national hospital discharge registry and to the Social Insurance Institution of Finland register for reimbursement of medicine expenses. There were no losses to follow-up.


Variables with skewed distributions (ie, alcohol consumption, physical activity) were natural log transformed to achieve approximately symmetrical distributions. Descriptive statistics were used to summarize baseline data: mean ± SD or median (IQR) for continuous variables, and n (%) for categorical variables. To assess the cross-sectional associations of %age-predicted CRF with various risk markers, Pearson's correlation coefficients were estimated using linear regression models adjusted for age. For incident T2D, HR with 95% CI were estimated using Cox proportional hazard models, after confirming no major departure from the assumptions of proportionality of hazards using Schoenfeld residuals.33 To explore a potential nonlinear dose-response relationship between %age-predicted CRF and T2D risk, we constructed a restricted cubic spline with knots at the 5th, 35th, 65th, and 95th percentiles of the distribution of %age-predicted CRF in a multivariable adjusted model. We modeled %age-predicted CRF as both continuous (per SD increase) and categorical (quartiles) variables. Adjustment for confounders was made using two models: (model 1) systolic blood pressure (SBP), smoking status, total cholesterol, high-density lipoprotein cholesterol (HDL-C), FPG, family history of T2D, and history of hypertension and (model 2) model 1 plus alcohol consumption, SES, and PA. To quantify and correct for within-person variability (regression dilution bias) in CRF levels, which is the extent to which the CRF measurements of an individual vary around the long-term average exposure levels (“usual levels”),34 an adjusted regression dilution ratio (RDR) was calculated by regressing available repeat measurements of CRF on baseline values.35 The estimated disease association was then divided (log HR and its 95% CIs) by the RDR. Given that %age-predicted CRF was estimated from CRF values, the association of “usual levels” of %age-predicted CRF with T2D risk was estimated using the RDR derived from repeat measurements of CRF. We used formal tests of interaction tests to assess statistical evidence of effect modification by individual characteristics, such as age and other clinically relevant characteristics. To minimize any bias due to reverse causation, sensitivity analysis involved excluding the first 5 yr of follow-up. Direct comparisons were made to the association of absolute CRF with incident T2D risk in the same sample of participants. All statistical analyses were conducted using Stata MP version 16 (Stata Corp).


The age, %age-predicted CRF, absolute CRF, and RER at baseline were 53 ± 5 yr, 87.0 ± 21.3%, 30.6 ± 7.9 mL/kg/min and 1.09 ± 0.14, respectively (Table 1). Men who developed T2D had lower values of %age-predicted CRF and HDL-C and higher values of body mass index and blood pressure at baseline. They were also more likely to have a history of hypertension and a family history of T2D (Table 1).

Table 1 - Baseline Participant Characteristicsa
Overall (n = 1901) Did Not Develop T2D (n = 1674) Developed T2D (n = 227) P Value
Exercise testing measures
Percentage of age-predicted CRF 87.0 ± 21.3 87.8 ± 21.5 80.7 ± 18.6 <.001
Absolute CRF, mL/kg/min 30.6 ± 7.9 30.9 ± 8.0 28.7 ± 7.2 <.001
Metabolic equivalentsb 8.74 ± 2.27 8.82 ± 2.29 8.20 ± 2.06 <.001
Peak respiratory gas exchange ratio 1.09 (0.14) 1.10 (0.14) 1.08 (0.13) .08
Questionnaire/prevalent conditions
Age at survey, yr 53 ± 5 53 ± 5 52 ± 5 .20
Alcohol consumption, g/wk 42.5 (12.4, 103.2) 42.4 (12.4, 101.6) 44.7 (12.1, 120.0) .95
Socioeconomic status 8.18 ± 4.26 8.18 ± 4.28 8.18 ± 4.16 <.99
Smoking status .065
No 1253 (65.9) 1091 (65.2) 162 (71.4)
Current 648 (34.1) 583 (34.8) 65 (28.6)
Family history of T2D <.001
No 1389 (73.1) 1245 (74.4) 144 (63.4)
Yes 512 (26.9) 429 (25.6) 83 (36.6)
History of hypertension .004
No 1360 (71.5) 1216 (72.6) 144 (63.4)
Yes 541 (28.5) 458 (27.4) 83 (36.6)
Physical measurements
BMI, kg/m2 26.8 ± 3.4 26.5 ± 3.3 28.8 ± 3.6 <.001
SBP, mm Hg 134 ± 16 133 ± 16 137 ± 17 .003
DBP, mm Hg 89 ± 10 88 ± 10 92 ± 9 <.001
Physical activity, KJ/d 1231 (662, 2000) 1245 (680, 1992) 1094 (534, 2111) .059
Blood-based markers
Total cholesterol, mmol/L 5.94 ± 1.07 5.93 ± 1.06 6.02 ± 1.13 .25
HDL-C, mmol/L 1.30 ± 0.31 1.31 ± 0.31 1.22 ± 0.27 <.001
Fasting plasma glucose, mmol/L 5.22 ± 0.85 5.12 ± 0.61 5.97 ± 1.64 <.001
Abbreviations: BMI, body mass index; CRF, cardiorespiratory fitness; DBP, diastolic blood pressure; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; T2D, type 2 diabetes.
aData are presented as mean ± SD, or median IQR, or n (%).
bOne metabolic equivalent corresponds to an oxygen uptake of 3.5 mL/kg/min during maximal exercise test.

Repeat measurements of CRF taken at 11 yr after baseline were available in a random sample of 505 men. The overall age-adjusted RDR of CRF was 0.58 (95% CI, 0.52-0.64), suggesting that using single baseline measurements of the exposures could underestimate the associations of %age-predicted CRF and absolute CRF with risk of T2D by [(1/0.58) − 1] × 100 = 72.4%.

During a median (IQR) follow-up of 26.8 (17.7-31.1) yr (45 182 person-yr at risk), 227 cases of T2D (annual rate 5.02/1000 person-yr at risk, 95% CI, 4.41-5.72) were recorded. A multivariable restricted cubic spline curve showed the risk of T2D decreased linearly with increasing %age-predicted CRF across the range 71-190 (P value for nonlinearity = .30) (Figure 1). In analysis adjusted for SBP, smoking status, total cholesterol, HDL-C, FPG, family history of T2D, and history of hypertension, the HR (95% CI)/1-SD increase in %age-predicted CRF for incident T2D was 0.67 (0.58-0.78), which persisted 0.68 (0.59-0.79) after further adjustment for alcohol consumption, SES, and PA. Alternatively, comparing the top versus bottom tertiles of %age-predicted CRF, the corresponding adjusted HR (95% CI) for incident T2D were 0.49 (0.34-0.71) and 0.51 (0.35-0.75), respectively (Table 2). The risk estimates were stronger on correction for regression dilution bias (Table 2). The association between %age-predicted CRF and T2D risk did not vary significantly by levels or categories of several clinically relevant characteristics (Figure 2). The inverse relationship between %age-predicted CRF and T2D risk persisted in analyses that excluded the first 5 yr of follow-up in the whole population (see Supplemental Digital Content 3, available at:

Figure 1.:
Restricted cubic splines of the HR of incident type 2 diabetes with %age-predicted CRF. Abbreviations: CRF, cardiorespiratory fitness; T2D, type 2 diabetes. Reference value for %age-predicted CRF is 52; dashed lines represent the 95% CIs for the spline model (solid line). Models were adjusted for systolic blood pressure, smoking status, total cholesterol, high-density lipoprotein cholesterol, fasting plasma glucose, family history of type 2 diabetes, history of hypertension, alcohol consumption, socioeconomic status and physical activity.
Table 2 - Association Between Percentage of Age-Predicted CRF and Risk of Type 2 Diabetes
Percentage of Age-Predicted CRF Events/Total Model 1a Model 2b
HR (95% CI) P Value HR (95% CI) P Value
Baseline %age-predicted CRF
Per 1-SD increase 227/1901 0.67 (0.58-0.78) <.001 0.68 (0.59-0.79) <.001
Tertile 1 (16.95-78.06) 94/634 Reference Reference
Tertile 2 (78.07-94.78) 85/634 0.88 (0.65-1.20) .43 0.91 (0.67-1.23) .53
Tertile 3 (>94.78) 48/633 0.49 (0.34-0.71) <.001 0.51 (0.35-0.75) .001
Usual %age-predicted CRFc
Per 1-SD increase 227/1901 0.50 (0.39-0.65) <.001 0.52 (0.40-0.67) <.001
Tertile 1 (16.95-78.06) 94/634 Reference Reference
Tertile 2 (78.07-94.78) 85/634 0.81 (0.6548-1.37) .43 0.84 (0.50-1.43) .53
Tertile 3 (>94.78) 48/633 0.29 (0.15-0.55) <.001 0.32 (0.17-0.60) .001
Abbreviations: CRF, cardiorespiratory fitness; %age-predicted CRF, percentage of age-predicted cardiorespiratory fitness.
aAdjusted for systolic blood pressure, smoking status, total cholesterol, high-density lipoprotein cholesterol, fasting plasma glucose, family history of type 2 diabetes, and history of hypertension.
bModel 1 plus alcohol consumption, socioeconomic status, and physical activity.
cCorrection for within-person variability in values of CRF, that is, the extent to which an individual's CRF measurements vary around a long-term average value (“usual CRF values”).

Figure 2.:
Association of percentage of age-predicted CRF with incident type 2 diabetes in clinically relevant subgroups. HRs were adjusted for systolic blood pressure, smoking status, total cholesterol, high-density lipoprotein cholesterol, fasting plasma glucose, family history of type 2 diabetes, history of hypertension, alcohol consumption, socioeconomic status, and physical activity. Abbreviations: CRF, cardiorespiratory fitness; HDL, high-density lipoprotein; T2D, type 2 diabetes. P value for interaction; cut-offs used for age, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol; and physical activity are median values.

In analyses that assessed the association of absolute CRF with incident T2D risk in the same set of study participants with consistent adjustment for confounders (including age), the HR (95% CI) were 0.71 (0.60-0.83) for a 1-SD increase in CRF and 0.64 (0.44-0.95) comparing the top versus bottom tertiles of CRF (Table 3). The risk estimates were stronger on correction for regression dilution bias (Table 3).

Table 3 - Association Between Absolute CRF and Risk of Incident Type 2 Diabetes
Absolute CRF, mL/kg/min Events/Total Model 1a Model 2b
HR (95% CI) P Value HR (95% CI) P Value
Baseline CRF
Per 1-SD increase 227/1901 0.69 (0.59-0.81) <.001 0.71 (0.60-0.83) <.001
Tertile 1 (6.36-27.13) 86/634 Reference Reference
Tertile 2 (27.14-33.62) 86/634 1.01 (0.74-1.39) .94 1.05 (0.76-1.44) .79
Tertile 3 (>33.62) 55/633 0.61 (0.41-0.89) .01 0.64 (0.44-0.95) .025
Usual CRFc
Per 1-SD increase 227/1901 0.53 (0.40-0.70) <.001 0.55 (0.42-0.73) <.001
Tertile 1 (6.36-27.13) 86/634 Reference Reference
Tertile 2 (27.14-33.62) 86/634 1.02 (0.59-1.77) .94 1.08 (0.62-1.87) .79
Tertile 3 (>33.62) 55/633 0.42 (0.22-0.81) .01 0.47 (0.24-0.91) .025
Abbreviation: CRF, cardiorespiratory fitness.
aAdjusted for age, systolic blood pressure, smoking status, total cholesterol, high-density lipoprotein cholesterol, fasting plasma glucose, family history of type 2 diabetes, and history of hypertension.
bModel 1 plus alcohol consumption, socioeconomic status, and physical activity.
cCorrection for within-person variability in values of CRF, that is, the extent to which an individual's CRF measurements vary around a long-term average value (“usual CRF values”)


In this population-based cohort study of Caucasian men without a history of T2D at baseline, higher %age-predicted CRF was associated with a lower risk of incident T2D, which was independent of several established risk factors and consistent with a linear dose-response relationship. The association of %age-predicted CRF with incident T2D risk did not differ by several clinically relevant subgroups and in sensitivity analysis that excluded the first 5 yr of follow-up. Further analyses in the same sample of participants showed significant evidence of an association between absolute CRF and T2D risk, but the risk estimates were more extreme for %age-predicted CRF and T2D risk, suggesting that %age-predicted CRF might be a stronger risk indicator for T2D than absolute CRF. In an analysis that corrected for within-person variability in CRF values, we also observed that using only single baseline measurements of %age-predicted CRF to assess the association may underestimate the strength of the association by 72%.

Although CRF is determined by many unmodifiable factors such as age, sex, and genetic factors, the most established ways of increasing CRF are through increased habitual PA and exercise training.6 Apart from habitual PA and exercise training, other related determinants of CRF include baseline health and fitness status of the individual, type, duration, and intensity of PA. It has been reported that 1-MET gain in CRF levels may be achieved through structured exercise for 4-5 mo irrespective of age, sex, weight status, and a history of PA.6,36 The beneficial effects of %age-predicted CRF on T2D risk may be via the effects of PA or exercise training. Indeed, it has been reported that most different types of PA (eg, total PA, leisure-time activity, resistance exercise, occupational activity, and walking) are associated with a 25-40% reduction in the relative risk of T2D.4 Both PA and exercise training may contribute to a reduction in the risk of T2D via improvement in energy balance, reduction in adiposity37 and inflammatory markers such as interleukin-19 and C-reactive protein,38,39 improvement in glucose homeostasis,40 insulin sensitivity, glycemic control and the metabolic profile,41,42 transformation of muscle fibers, increased mitochondrial activity and content, and increases in GLUT4 protein expression.40 The improvements in insulin sensitivity and blood glucose reductions have been reported to be related to the duration and intensity of exercise, with greater effects attributed to more prolonged and intense PA than for nonvigorous PA.42 This is consistent with the results from our restricted cubic spline curve, which showed no threshold effect, suggesting that the risk of T2D decreases continuously with increase in levels of %age-predicted CRF from low to high. Furthermore, the preventative cardioprotective effect of CRF has been suggested to be partly explained by healthy vascular aging, which confers higher CRF.43

There is limited data on the potential clinical application of %age-predicted CRF as a useful exercise test parameter. Only a few studies including some of ours have evaluated associations between %age-predicted CRF and outcomes such as CVD mortality, sudden cardiac death, hypertension, and all-cause mortality.18–21 Majority of studies evaluating CPX parameters have focused on absolute CRF values. So far, the evidence seems to suggest that %age-predicted CRF (ie, values of CRF, which take into account the influence of age) is comparable or may be superior to absolute CRF as a risk indicator or predictor for adverse cardiometabolic outcomes.18–20 The strong and graded nature of the relationship between %age-predicted CRF and T2D risk suggests that %age-predicted CRF may be potentially suitable for population-level risk assessment. There have been continued calls for research into the clinical utility of CPX parameters across all patient populations.44 These findings should stimulate more research into the use of %age-predicted CRF, which may be a more reliable risk factor for chronic disease outcomes, including T2D.


This is the first evaluation of the association of %age-predicted CRF with the risk of incident T2D as well as comparison of the association with that of absolute CRF and T2D risk in the same sample of study participants. Other strengths included the use of the gold standard objective approach of assessing CRF, the relatively large population-based cohort, which was also representative of a general population of middle-aged and older Finnish men, exclusion of men with prevalent T2D at baseline, adjustment for several potential confounders, and further association analyses including evaluating the nature of the dose-response relationship, assessing effect modification by clinically relevant characteristics, and minimizing reverse causation by conducting sensitivity analyses, which involved excluding the first 5 yr of follow-up. Repeat measurements of CRF made within a random subset of individuals over time after baseline were available, which enabled correction for the extent of within-person variability in CRF values over the long period of follow-up. Corrections could only be done using the RDR and not a time-varying analysis, because repeat measurements were only available in a random subset of participants. The limitations were mostly inherent to the study design and included (i) potential biases in observational studies such as residual confounding due to errors in risk marker measurements and unmeasured confounding; (ii) the possibility that lifestyle habits may have changed during follow-up due to probable changes in health habits or other incident diseases occurring over the long period; and (iii) inability to generalize the findings to women and other populations and age groups. The exposure—age-predicted CRF—was estimated from an age-based prediction equation. It is acknowledged that nonexercise prediction equations have inherent limitations such as inability to account for the well-established genetic influence on CRF, influence of social desirability biases on self-reported aspects of some of the equations, and underestimation and overestimation of CRF at the top and bottom ends of the distribution, respectively.6,45–47 However, it is also well known that most of these non-exercise-based algorithms can conveniently estimate CRF in a rapid, inexpensive and reasonably accurate way when used for large population settings.6


Percentage of age-predicted CRF is inversely and independently associated with risk of incident T2D in middle-aged and older men, consistent with a linear dose-response relationship. Furthermore, %age-predicted CRF may be a stronger risk indicator for T2D than absolute CRF.


We thank the staff of the Kuopio Research Institute of Exercise Medicine and the Research Institute of Public Health and University of Eastern Finland, Kuopio, Finland, for the data collection in the study. Professor Laukkanen is supported by the Finnish Foundation for Cardiovascular Research, Helsinki, Finland.


1. Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;389(10085):2239–2251.
2. Seuring T, Archangelidi O, Suhrcke M. The economic costs of type 2 diabetes: a global systematic review. Pharmacoeconomics. 2015;33(8):811–831.
3. Gray LJ, Taub NA, Khunti K, et al. The Leicester Risk Assessment score for detecting undiagnosed type 2 diabetes and impaired glucose regulation for use in a multiethnic UK setting. Diabet Med. 2010;27(8):887–895.
4. 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):529–542.
5. Franklin BA, Brubaker PH, Harber MP, Lavie CJ, Myers J, Kaminsky LA. The Journal of Cardiopulmonary Rehabilitation and Prevention at 40 years and its role in promoting lifestyle medicine for prevention of cardiovascular diseases: part 1. J Cardiopulm Rehabil Prev. 2020;40(3):131–137.
6. Ross R, Blair SN, Arena R, et al. Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the American Heart Association. Circulation. 2016;134(24):e653–e699.
7. Noonan V, Dean E. Submaximal exercise testing: clinical application and interpretation. Phys Ther. 2000;80(8):782–807.
8. Kodama S, Saito K, Tanaka S, et al. Cardiorespiratory fitness as a quantitative predictor of all-cause mortality and cardiovascular events in healthy men and women: a meta-analysis. JAMA 2009;301(19):2024–2035.
9. Khan H, Kunutsor SK, Rauramaa R, Merchant FM, Laukkanen JA. Long-term change in cardiorespiratory fitness in relation to atrial fibrillation and heart failure (from the Kuopio Ischemic Heart Disease Risk Factor Study). Am J Cardiol. 2018;121(8):956–960.
10. Kunutsor SK, Khan H, Laukkanen T, Laukkanen JA. Joint associations of sauna bathing and cardiorespiratory fitness on cardiovascular and all-cause mortality risk: a long-term prospective cohort study. Ann Med. 2018;50(2):139–146.
11. Laukkanen J, Laukkanen T, Khan H, Babar M, Kunutsor SK. Combined effect of sauna bathing and cardiorespiratory fitness on the risk of sudden cardiac deaths in Caucasian men: a long-term prospective cohort study. Prog Cardiovasc Dis. 2018;60(6):635–641.
12. Laukkanen JA, Lavie CJ, Khan H, Kurl S, Kunutsor SK. Cardiorespiratory fitness and the risk of serious ventricular arrhythmias: a prospective cohort study. Mayo Clin Proc. 2019;94(5):833–841.
13. Laukkanen JA, Isiozor NM, Kunutsor SK. Objectively assessed cardiorespiratory fitness and all-cause mortality risk: an updated meta-analysis of 37 cohort studies involving 2,258,029 million participants. Mayo Clin Proc. 2022;97(6):1038–1040.
14. Zaccardi F, O'Donovan G, Webb DR, 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):131–137.
15. Bouchard C. Genomic predictors of trainability. Exp Physiol. 2012;97(3):347–352.
16. Kaminsky LA, Arena R, Myers J, et al. Updated reference standards for cardiorespiratory fitness measured with cardiopulmonary exercise testing: data from the Fitness Registry and the Importance of Exercise National Database (FRIEND). Mayo Clin Proc. 2022;97(2):285–293.
17. Kokkinos P, Faselis C, Myers J, Sui X, Zhang J, Blair SN. Age-specific exercise capacity threshold for mortality risk assessment in male veterans. Circulation. 2014;130(8):653–658.
18. Laukkanen JA, Kunutsor SK. Percentage of age-predicted cardiorespiratory fitness and risk of incident hypertension: a prospective cohort study. J Cardiopulm Rehabil Prev. 2022;42(4):272–277.
19. Laukkanen JA, Kurl S, Khan H, Kunutsor SK. Percentage of age-predicted cardiorespiratory fitness and risk of sudden cardiac death: a prospective cohort study. Heart Rhythm. 2021;18(7):1171–1177.
20. Laukkanen JA, Kurl S, Khan H, Zaccardi F, Kunutsor SK. Percentage of age-predicted cardiorespiratory fitness is inversely associated with cardiovascular disease mortality: a prospective cohort study. Cardiology. 2021;146(5):616–623.
21. Myers J, Prakash M, Froelicher V, Do D, Partington S, Atwood JE. Exercise capacity and mortality among men referred for exercise testing. N Engl J Med. 2002;346(11):793–801.
22. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344–349.
23. Lakka TA, Venalainen JM, Rauramaa R, Salonen R, Tuomilehto J, Salonen JT. Relation of leisure-time physical activity and cardiorespiratory fitness to the risk of acute myocardial infarction. N Engl J Med. 1994;330(22):1549–1554.
24. Kunutsor SK, Makikallio TH, Araujo CGS, Jae SY, Kurl S, Laukkanen JA. Cardiorespiratory fitness is not associated with risk of venous thromboembolism: a cohort study. Scand Cardiovasc J. 2019;53(5):255–258.
25. Kunutsor SK, Laukkanen T, Laukkanen JA. Cardiorespiratory fitness is associated with reduced risk of future psychosis: a long-term prospective cohort study. Schizophr Res. 2018;192:473–474.
26. Morris CK, Myers J, Froelicher VF, Kawaguchi T, Ueshima K, Hideg A. Nomogram based on metabolic equivalents and age for assessing aerobic exercise capacity in men. J Am Coll Cardiol. 1993;22(1):175–182.
27. de Souza ESCG, Kaminsky LA, Arena R, et al. A reference equation for maximal aerobic power for treadmill and cycle ergometer exercise testing: analysis from the FRIEND registry. Eur J Prev Cardiol. 2018;25(7):742–750.
28. Salonen JT, Nyyssonen K, Korpela H, Tuomilehto J, Seppanen R, Salonen R. High stored iron levels are associated with excess risk of myocardial infarction in eastern Finnish men. Circulation. 1992;86(3):803–811.
29. Kunutsor SK, Khan H, Laukkanen JA. Serum albumin concentration and incident type 2 diabetes risk: new findings from a population-based cohort study. Diabetologia. 2015;58(5):961–967.
30. Salonen JT, Salonen R, Seppanen K, Rauramaa R, Tuomilehto J. HDL, HDL2, and HDL3 subfractions, and the risk of acute myocardial infarction. A prospective population study in eastern Finnish men. Circulation. 1991;84(1):129–139.
31. Kunutsor SK, Jae SY, Makikallio TH, Laukkanen JA. High fitness levels attenuate the increased risk of heart failure due to low socioeconomic status: a cohort study. Eur J Clin Invest. 2022;52(6):e13744.
32. Taylor HL, Jacobs DR Jr, Schucker B, Knudsen J, Leon AS, Debacker G. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis. 1978;31(12):741–755.
33. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York, NY: Springer; 2000.
34. Fibrinogen Studies C, Wood AM, White I, Thompson SG, Lewington S, Danesh J. Regression dilution methods for meta-analysis: assessing long-term variability in plasma fibrinogen among 27,247 adults in 15 prospective studies. Int J Epidemiol. 2006;35(6):1570–1578.
35. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med. 1989;8(9):1051–1069; discussion 1071-1073.
36. Lin X, Zhang X, Guo J, 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):e002014.
37. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in women and men. N Engl J Med. 2011;364(25):2392–2404.
38. Ford ES. Does exercise reduce inflammation? Physical activity and C-reactive protein among U.S. adults. Epidemiology. 2002;13(5):561–568.
39. Church TS, Barlow CE, Earnest CP, Kampert JB, Priest EL, Blair SN. Associations between cardiorespiratory fitness and C-reactive protein in men. Arterioscler Thromb Vasc Biol. 2002;22(11):1869–1876.
40. Rockl KS, Witczak CA, Goodyear LJ. Signaling mechanisms in skeletal muscle: acute responses and chronic adaptations to exercise. IUBMB Life. 2008;60(3):145–153.
41. Boule NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: a meta-analysis of controlled clinical trials. JAMA. 2001;286(10):1218–1227.
42. Mayer-Davis EJ, D'Agostino R Jr, Karter AJ, et al. Intensity and amount of physical activity in relation to insulin sensitivity: the Insulin Resistance Atherosclerosis Study. JAMA. 1998;279(9):669–674.
43. Fleenor BS, Carlini NA, Kaminsky LA, Whaley MH, Peterman JE, Harber MP. Healthy vascular aging is associated with higher cardiorespiratory fitness. J Cardiopulm Rehabil Prev. 2021;41(2):122–125.
44. Guazzi M, Arena R, Halle M, Piepoli MF, Myers J, Lavie CJ. 2016 focused update: clinical recommendations for cardiopulmonary exercise testing data assessment in specific patient populations. Eur Heart J. 2016;39(14):1144–1161.
45. Peterman JE, Harber MP, Imboden MT, et al. Accuracy of nonexercise prediction equations for assessing longitudinal changes to cardiorespiratory fitness in apparently healthy adults: BALL ST cohort. J Am Heart Assoc. 2020;9(11):e015117.
46. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status, limitations, and future directions. Res Q Exerc Sport. 2000;71(2 suppl):S1–S14.
47. Bouchard C, Daw EW, Rice T, et al. Familial resemblance for V˙o2max in the sedentary state: the HERITAGE family study. Med Sci Sports Exerc. 1998;30(2):252–258.

cardiopulmonary exercise testing; cohort study; percentage of age-predicted cardiorespiratory fitness; type 2 diabetes

Supplemental Digital Content

© 2022 Wolters Kluwer Health, Inc. All rights reserved.