Cardiorespiratory fitness and type 2 diabetes risk: A prospective cohort study with mediation analysis of biological aging in the UK Biobank

Xiangliang Liu; Xinqiao Chen; Wang Yang; Guanran Ding; Yixin Zhao; Yuguang Li; Naifei Chen; Jiuwei Cui

doi:10.1111/dom.70261

. 2025 Nov 3;28(2):914–924. doi: 10.1111/dom.70261

Cardiorespiratory fitness and type 2 diabetes risk: A prospective cohort study with mediation analysis of biological aging in the UK Biobank

Xiangliang Liu ¹, Xinqiao Chen ¹, Wang Yang ¹, Guanran Ding ¹, Yixin Zhao ¹, Yuguang Li ^1,^✉, Naifei Chen ^1,^✉, Jiuwei Cui ^1,^✉

PMCID: PMC12803562 PMID: 41178744

Abstract

Aims

Higher cardiorespiratory fitness is associated with reduced type 2 diabetes mellitus (T2D) risk, but the underlying mechanisms remain incompletely understood. We investigated whether biological aging mediates the association between maximal oxygen uptake (VO₂ max) and incident T2D risk.

Materials and Methods

This prospective cohort study included 54 418 UK Biobank participants aged 39–70 years without baseline diabetes. VO₂max was estimated using a validated algorithm incorporating resting heart rate, physical activity, age, sex and body mass index. Biological age (BA) and phenotypic age (PhenoAge) were calculated from clinical biomarkers. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for sociodemographic, lifestyle and clinical factors. Linear regression analyses assessed cross‐sectional associations between VO₂ max and standardised glycaemic and lipid biomarkers. Mediation analysis quantified the proportion of association explained by biological aging measures.

Results

During 694 986 person‐years of follow‐up, 2628 participants developed T2D (incidence rate: 3.78 per 1000 person‐years). Compared to the lowest VO₂ max quartile, participants in the highest quartile had a 56% lower T2D risk (HR 0.44, 95% CI = 0.39–0.50). Each standard deviation increase in VO₂ max was associated with a 28% lower risk (HR 0.72, 95% CI = 0.68–0.76). BA acceleration mediated 8.2% (95% CI = 6.1%–10.8%) and PhenoAge acceleration mediated 9.1% (95% CI = 6.8%–12.1%) of the VO₂ max–T2D association. Protective associations were consistent across sex, age, ethnicity and genetic risk subgroups. VO₂ max showed strong inverse correlations with glucose (β = −0.32), glycated haemoglobin (β = −0.28), triglycerides (β = −0.31) and a positive correlation with high‐density lipoprotein (HDL) cholesterol (β = 0.29).

Conclusions

Higher cardiorespiratory fitness demonstrates robust protective associations against T2D incidence, with biological aging mechanisms partially mediating this relationship.

Keywords: biological aging, cardiorespiratory fitness, maximal oxygen uptake, mediation analysis, primary prevention, prospective cohort study, type 2 diabetes mellitus, UK Biobank

1. INTRODUCTION

The global incidence, mortality and disease burden of diabetes are continuously rising, with men and low‐middle socio‐demographic index (SDI) countries most affected, and an estimated 1.195 billion non‐elderly individuals projected to have type 2 diabetes (T2D) by 2050, highlighting a major global public health challenge. ¹ This metabolic disorder substantially increases risks of cardiovascular disease, nephropathy, retinopathy and premature mortality, imposing enormous healthcare and economic burdens globally. ² , ³ While genetic predisposition contributes significantly to T2D risk, modifiable lifestyle factors offer promising avenues for prevention and management. ⁴ , ⁵

Cardiorespiratory fitness, objectively quantified by maximal oxygen consumption (VO₂max), represents a comprehensive physiological marker reflecting cardiovascular, pulmonary and skeletal muscle function. ⁶ Mounting epidemiological evidence demonstrates robust inverse associations between higher cardiorespiratory fitness and T2D incidence across diverse populations. ⁷ , ⁸ , ⁹ A meta‐analysis of 10 prospective studies encompassing over 300 000 participants revealed that individuals in the highest fitness quintile experienced 42% lower T2D risk compared to the lowest quintile. ¹⁰ Similarly, the Aerobics Center Longitudinal Study demonstrated that low cardiorespiratory fitness was associated with increased risk for impaired fasting glucose and T2D. ¹¹

The protective mechanisms underlying the fitness–diabetes relationship involve multiple interconnected pathways. Enhanced cardiorespiratory fitness improves insulin sensitivity through increased glucose transporter‐4 translocation, enhanced mitochondrial biogenesis and optimised substrate utilisation. ¹² , ¹³ Additionally, higher fitness levels are associated with favourable alterations in inflammatory markers, adipokine profiles and oxidative stress parameters that collectively contribute to improved metabolic health. ¹⁴ , ¹⁵ Crucially, cardiorespiratory fitness and biological aging share intersecting biological pathways directly relevant to T2D pathogenesis. Higher fitness improves mitochondrial function, reduces oxidative stress and alleviates chronic inflammation—all hallmarks of accelerated biological aging. Conversely, accelerated biological aging manifests as dysregulated metabolic homeostasis. However, the precise mechanisms mediating these associations remain incompletely understood, particularly regarding the role of biological aging processes.

Biological aging, distinct from chronological aging, reflects the cumulative physiological deterioration across multiple organ systems. ¹⁶ Recent advances in aging research have established that biological age (BA) acceleration—defined as the deviation of BA from chronological age—serves as a robust predictor of age‐related disease risk and mortality. ¹⁷ , ¹⁸ Two prominent measures, BA derived from clinical biomarkers and phenotypic age (PhenoAge) incorporating mortality risk algorithms, have demonstrated superior predictive capacity for health outcomes compared to chronological age alone. ¹⁹ , ²⁰ Emerging evidence suggests that lifestyle interventions, including exercise training, can decelerate biological aging processes. ²¹ , ²² However, whether biological aging mediates the relationship between cardiorespiratory fitness and T2D risk remains unexplored.

The UK Biobank provides an exceptional opportunity to investigate these relationships given its large‐scale prospective design, comprehensive phenotypic characterisation and extended follow‐up duration. ²³ Previous UK Biobank analyses have demonstrated protective associations between physical activity and diabetes risk, ²⁴ , ²⁵ but studies specifically examining objectively measured VO₂max and its mechanistic pathways through biological aging are limited.

Furthermore, considerable heterogeneity exists in fitness–diabetes associations across demographic and clinical subgroups. Sex‐specific differences in metabolic responses to exercise, ethnic variations in diabetes susceptibility and genetic predisposition through polygenic risk scores (PRS) may modify the protective effects of cardiorespiratory fitness. ²⁶ , ²⁷ , ²⁸ Understanding these effect modifications is crucial for developing personalised prevention strategies and identifying high‐risk populations who might derive maximum benefit from fitness interventions.

Given these knowledge gaps, we conducted a comprehensive analysis in the UK Biobank cohort to: quantify the association between objectively measured VO₂max and incident T2D risk; examine whether biological aging mechanisms mediate this relationship; investigate effect modification across demographic, lifestyle and genetic strata; and explore dose–response relationships and potential nonlinear associations. We hypothesised that higher VO₂max would demonstrate strong protective associations against T2D incidence, with BA acceleration serving as a significant mediating pathway.

2. METHOD

2.1. Study design and population

The UK Biobank is a prospective cohort study of over 500 000 individuals recruited across the United Kingdom between 2006 and 2010. During baseline assessment, participants provided written informed consent, completed a touch‐screen questionnaire detailing demographic characteristics, lifestyle habits and medical history, underwent standardised anthropometric measurements and contributed biological specimens. ²³ After excluding individuals with insufficient baseline data for calculating the VO₂max (n = 436 776), individuals lacking follow‐up data for T2D (n = 9416), missing data on covariates (n = 1579), the analytic cohort comprised 54 418 participants. Following further exclusion for missing biological aging data, the BA subgroup included 46 475 participants and the PhenoAge subgroup included 51 060 participants (Figure S1). Ethical approval for UK Biobank was granted by the North West Multi‐Centre Research Ethics Committee, and all participants provided written informed consent.

2.2. Assessment of VO₂max

In UK Biobank participants who completed an exercise electrocardiogram (ECG) test, maximal oxygen consumption (VO₂max) was estimated according to the multilevel modelling framework developed and validated by Gonzales et al. ²⁹ This framework incorporates individual heart rate responses relative to workload progression during exercise.

2.3. Assessment of biological age and phenotypic age

BA was computed via multiple linear regression of selected biomarkers against chronological age, representing the chronological age at which an individual's biomarker profile corresponds to typical physiological function. Specifically, BA computation utilised nine biomarkers: forced expiratory volume in 1 s (FEV1), systolic blood pressure (SBP), albumin, alkaline phosphatase (ALP), blood urea nitrogen (BUN), creatinine, C‐reactive protein (CRP), glycated haemoglobin (HbA1c) and total cholesterol. ³⁰

In parallel, PhenoAge was derived from a mortality‐prediction algorithm incorporating both biomarkers and chronological age, and is interpreted as the chronological age matching an individual's predicted mortality risk to the population average risk. Notably, PhenoAge incorporated the following components: albumin, creatinine, glucose, CRP, mean corpuscular volume (MCV), lymphocyte percentage, red cell distribution width (RDW), ALP, white blood cell count (WBC) and chronological age. Detailed computational procedures are provided in Methods, Supporting Information S1.

Age acceleration was calculated as the residuals from the regression of BA and PhenoAge, respectively, against chronological age, which reflect the degree of deviation of an individual's BA or PhenoAge from their chronological age. Comprehensive methodological details and interpretations of both measures are previously documented. ³¹

2.4. Assessment of polygenic risk score

The standard PRS for T2D were constructed using the UK Biobank PRS database. This framework integrates cumulative genetic effects into a composite predisposition metric. ³² Individuals were stratified into distinct genetic susceptibility categories based on PRS percentile thresholds. Notably, higher PRS values correlate with increased genetic predisposition to T2D.

2.5. Incident T2D

Follow‐up occurrences of T2D were ascertained by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD‐10) codes E11 (non‐insulin‐dependent diabetes mellitus). Follow‐up duration was defined as the period from UK Biobank enrolment until the earliest occurrence of T2D, death or study termination (September 2024).

2.6. Covariates

Covariates included demographic characteristics (age, sex, ethnicity and educational attainment), lifestyle behaviours (alcohol consumption and smoking status), biochemical measures (body mass index [BMI]) and clinical indicators (overall health rating). Ethnicity was categorised as White; Mixed (including White and Black Caribbean, White and Black African, White and Asian or any other mixed background); Asian; Black or Other. Educational attainment comprised college/university education; high school education (defined as A levels/AS levels or equivalent); or less than high school education. Alcohol consumption was grouped as never, former or current drinkers. Smoking status was classified as Never or Ever (encompassing former and current smokers). Self‐rated overall health was assessed across four levels: excellent, good, fair and poor.

2.7. Statistical analysis

Demographic and clinical characteristics of the study population were summarised as mean ± standard deviation (SD) for continuous variables and as frequency (percentage) for categorical variables. Between‐group comparisons were performed using the Student t test for normally distributed continuous variables and the Mann–Whitney U test for non‐normally distributed variables. Categorical variables were evaluated using the χ ² test or Fisher exact test.

Cox proportional hazards regression models were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between VO₂max and incident T2D. The linearity assumption between VO₂max and glycaemic/lipid biomarkers was assessed by visually inspecting scatter plots with fitted Loess curves. Linear regression analyses were subsequently performed to evaluate associations of standardised glycaemic/lipid biomarkers with VO₂max. Model 1 was the unadjusted crude model. Model 2 was further adjusted for sex, age and ethnicity. Model 3 was fully adjusted for smoking status, alcohol status, education level, BMI and overall health rating based on the adjustments made in Model 2. Potential nonlinear relationships were explored with restricted cubic splines (RCS). Subgroup analyses with multiplicative interaction terms were implemented to evaluate effect modification across demographic and clinical strata. Moreover, mediation analysis was conducted to determine whether BA and PhenoAge mediated the relationship between VO₂max and T2D risk. Sensitivity analyses were performed to examine the robustness of our primary findings: (1) exclusion of participants with outlying VO₂max values (<2.5th or >97.5th percentiles); (2) exclusion of T2D cases diagnosed within the first year of follow‐up; and (3) a covariate‐enhanced Cox model additionally adjusting for physical activity (moderate and vigorous metabolic equivalent task minutes per week), diet (energy from overall diet) and socioeconomic status (average total household income before tax). All analyses were two‐tailed, with p < 0.05 denoting statistical significance. Data processing and statistical computations were conducted in R (version 4.2.0; R Foundation for Statistical Computing).

3. RESULTS

3.1. Population characteristic

As displayed in Table 1, participants (N = 54 418) were stratified by VO₂max quartiles (Q1: lowest fitness to Q4: highest fitness; mean ± SD: 20.5 ± 3.0, 25.9 ± 1.2, 30.1 ± 1.3 and 37.0 ± 4.1 mL/kg/min). Higher VO₂max quartiles were associated with progressively younger ages, higher male predominance and greater educational attainment. Cardiometabolic profiles improved markedly across quartiles, showing lower BMI, reduced SBP, decreased low‐density lipoprotein (LDL), lower triglycerides and improved HbA1c. Biological aging indices declined substantially, paralleling improvements in self‐rated health and lung function.

TABLE 1.

Baseline characteristics.

		Q1	Q2	Q3	Q4	p
		(n = 13 770)	(n = 13 552)	(n = 13 519)	(n = 13 577)	p
Ethnicity (%)	Asian	405 (2.94)	395 (2.91)	452 (3.34)	430 (3.17)	<0.0001
	Black	255 (1.85)	118 (0.87)	128 (0.95)	83 (0.61)
	Mixed	72 (0.52)	79 (0.58)	67 (0.50)	94 (0.69)
	Other	578 (4.20)	402 (2.97)	342 (2.53)	296 (2.18)
	White	12 460 (90.49)	12 558 (92.67)	12 530 (92.68)	12 674 (93.35)
Age ^a		58.749 (7.525)	57.555 (7.860)	56.481 (8.107)	54.752 (8.235)	<0.0001
Educational level (%)	College	3732 (27.10)	4514 (33.31)	5043 (37.30)	6063 (44.66)	<0.0001
	High school	7403 (53.76)	7129 (52.60)	6826 (50.49)	6280 (46.25)
	Less than high school	2635 (19.14)	1909 (14.09)	1650 (12.21)	1234 (9.09)
Sex (%)	Female	12 259 (89.03)	9013 (66.51)	5658 (41.85)	2770 (20.40)	<0.0001
Sex (%)	Male	1511 (10.97)	4539 (33.49)	7861 (58.15)	10 807 (79.60)
Alcohol status (%)	Current	12 306 (89.37)	12 561 (92.69)	12 666 (93.69)	12 839 (94.56)	<0.0001
	Never	929 (6.75)	579 (4.27)	456 (3.37)	340 (2.50)
	Previous	535 (3.89)	412 (3.04)	397 (2.94)	398 (2.93)
Smoking (%)	No	6299 (45.74)	5314 (39.21)	5117 (37.85)	5069 (37.34)	<0.0001
Smoking (%)	Yes	7471 (54.26)	8238 (60.79)	8402 (62.15)	8508 (62.66)
BMI ^a		29.990 (5.197)	27.317 (4.036)	26.321 (3.617)	24.880 (3.106)	<0.0001
Overall health rating (%)	Excellent	1175 (8.53)	1697 (12.52)	2121 (15.69)	2957 (21.78)	<0.0001
	Fair	3769 (27.37)	3052 (22.52)	2695 (19.93)	2087 (15.37)
	Good	8243 (59.86)	8414 (62.09)	8366 (61.88)	8288 (61.04)
	Poor	583 (4.23)	389 (2.87)	337 (2.49)	245 (1.80)
VO₂max ^a		20.463 (3.043)	25.936 (1.176)	30.147 (1.322)	37.036 (4.077)	<0.0001
FEV1 ^a		2.283 (0.580)	2.569 (0.658)	2.854 (0.735)	3.174 (0.742)	<0.0001
SBP ^a		140.537 (17.344)	136.822 (17.638)	135.390 (17.219)	132.689 (15.844)	<0.0001
ALP ^a		90.800 (27.014)	84.247 (25.348)	80.923 (25.148)	78.266 (22.820)	<0.0001
BUN ^a		15.016 (3.810)	14.972 (3.720)	15.049 (3.698)	15.236 (3.637)	<0.0001
Creatinine ^a		0.766 (0.180)	0.804 (0.185)	0.849 (0.202)	0.881 (0.161)	<0.0001
CRP ^a		0.343 (0.468)	0.242 (0.409)	0.200 (0.345)	0.170 (0.370)	<0.0001
HbA1c ^a		5.526 (0.614)	5.432 (0.527)	5.384 (0.501)	5.303 (0.410)	<0.0001
Total cholesterol ^a		228.818 (45.119)	223.637 (43.927)	218.230 (41.567)	214.806 (40.357)	<0.0001
HDL ^a		1.522 (0.372)	1.513 (0.400)	1.469 (0.397)	1.470 (0.377)	<0.0001
LDL ^a		3.685 (0.887)	3.588 (0.858)	3.507 (0.816)	3.458 (0.808)	<0.0001
Triglycerides ^a		1.772 (0.950)	1.712 (0.989)	1.684 (1.015)	1.525 (0.891)	<0.0001
Albumin ^a		45.324 (2.612)	45.716 (2.562)	45.901 (2.601)	46.061 (2.594)	<0.0001
Glucose ^a		5.230 (1.008)	5.114 (0.926)	5.049 (0.778)	5.000 (0.698)	<0.0001
Lymphocyte percentage ^a		30.023 (7.672)	29.786 (7.575)	29.370 (7.708)	29.150 (7.532)	<0.0001
MCV ^a		85.020 (5.336)	85.352 (5.200)	85.424 (5.210)	85.660 (5.233)	<0.0001
RDW ^a		13.673 (1.099)	13.546 (1.018)	13.477 (0.917)	13.418 (0.837)	<0.0001
WBC ^a		7.461 (1.983)	7.148 (1.955)	6.982 (2.265)	6.629 (2.268)	<0.0001
BA ^a		58.057 (7.692)	55.715 (8.036)	53.531 (8.352)	50.845 (8.742)	<0.0001
PhenoAge ^a		49.654 (8.869)	47.132 (9.646)	45.747 (10.063)	43.421 (10.125)	<0.0001

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Abbreviations: ALP, alkaline phosphatase; BA, biological age; BMI, body mass index; BUN, blood urea nitrogen; CRP, C‐reactive protein; HbA1c, glycated haemoglobin; FEV1, forced expiratory volume in 1 s; MCV, mean corpuscular volume; RDW, red cell distribution width; SBP, systolic blood pressure; VO₂max, maximal oxygen consumption; WBC, white blood cell count.

^{^a}

Data were expressed as mean (SD) and used t test to test the difference in group distribution.

3.2. The association between VO₂ max and T2D

Higher VO₂max demonstrated a strong, graded inverse association with incident T2D across all models (Table 2). In unadjusted analyses, the HRs monotonically decreased per quartile elevation, with Q4 exhibiting 69% lower risk versus Q1 (HR = 0.31, 95% CI = 0.28–0.35, p < 0.001). In the fully adjusted model including clinical and lifestyle factors, a 56% reduction in T2D risk persisted for Q4 versus Q1 (HR = 0.44, 95% CI = 0.38–0.50, p < 0.001). Each 1‐SD increase in VO₂max independently conferred a 28% lower T2D risk (HR = 0.72, 95% CI = 0.68–0.76, p < 0.001) with a significant dose–response trend (p trend <0.001). The RCS confirmed a similar monotonic decreasing trend between VO₂max increment and T2D risk (Figure 1). Additionally, sensitivity analyses consistently confirmed a robust inverse association between higher VO₂max and reduced T2D risk (Table S1). After excluding incident cases within the first year, the multivariable‐adjusted HR for the highest VO₂max quartile (Q4 vs. Q1) was 0.49 (95% CI = 0.38–0.62, p < 0.001). Similarly, after trimming extreme VO₂max values, the corresponding adjusted HR was 0.46 (95% CI = 0.39–0.53, p < 0.001). Furthermore, the association retained significance in the covariate‐expanded model adjusting for physical activity, dietary intake and socioeconomic status. Each standard deviation increase in VO₂max was associated with 32% lower risk (HR = 0.68, 95% CI = 0.63–0.74), and a 60% reduction in T2D risk persisted for Q4 versus Q1 (HR = 0.40, 95% CI = 0.32–0.50, p < 0.001).

TABLE 2.

The relationship between VO₂max and type 2 diabetes mellitus.

	Model 1 (unadjusted)		Model 2 (demographics adjusted)		Model 3 (fully adjusted)
	HR (95% CI)	p Value	HR (95% CI)	p Value	HR (95% CI)	p Value
Per SD ^a	0.64 (0.62, 0.66)	<0.001	0.46 (0.44, 0.48)	<0.001	0.72 (0.68, 0.76)	<0.001
Q2	0.65 (0.60, 0.71)	<0.001	0.44 (0.41, 0.48)	<0.001	0.74 (0.67, 0.81)	<0.001
Q3	0.56 (0.51, 0.61)	<0.001	0.28 (0.25, 0.31)	<0.001	0.62 (0.55, 0.70)	<0.001
Q4	0.31 (0.28, 0.35)	<0.001	0.13 (0.12, 0.15)	<0.001	0.44 (0.38, 0.50)	<0.001
p Trend ^b	0.70 (0.68, 0.72)	<0.001	0.52 (0.50, 0.54)	<0.001	0.77 (0.74, 0.81)	<0.001

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Abbreviations: CI, confidence interval; HR, hazard ratio; SD, standard deviation.

^{^a}

The “Per SD” estimate comes from modelling the exposure as a continuous variable.

^{^b}

The “p for trend” and its HR come from modelling the exposure quartiles as an ordinal variable, representing the hazard ratio per one‐quartile increase.

Restricted cubic spline of the association between maximal oxygen consumption (VO₂max) and type 2 diabetes mellitus (T2D). CI, confidence interval; HR, hazard ratio.

3.3. The association between VO₂ max and glycaemic/lipid biomarkers

Linear regression analyses revealed inverse associations between glycaemic/lipid biomarkers (glucose, HbA1c, triglycerides, LDL and total cholesterol) and VO₂max, persisting after multivariable adjustment (Table 3). Per 1‐SD increase in VO₂max was inversely associated with glucose (β = −0.13, 95% CI = −0.14 to −0.11), total cholesterol (β = −0.06, 95% CI = −0.07 to −0.05), HbA1c (β = −0.07, 95% CI = −0.08 to −0.06), triglycerides (β = −0.14, 95% CI = −0.16 to −0.13) and LDL cholesterol (β = −0.06, 95% CI = −0.08 to −0.05), while being positively associated with HDL cholesterol (β = 0.07, 95% CI = 0.06–0.08; all p < 0.001). Quartile analyses revealed strict dose–response gradients across all biomarkers (p trend <0.001), with the highest quartile (Q4) exhibiting the strongest effects (glucose: β = −0.32, 95% CI = −0.35 to −0.28; triglycerides: β = −0.33, 95% CI = −0.37 to −0.30; HbA1c: β = −0.19, 95% CI = −0.22 to −0.15; LDL: β = −0.15, 95% CI = −0.18 to −0.11; total cholesterol: β = −0.15, 95% CI = −0.18 to −0.11; HDL: β = 0.13, 95% CI = 0.10–0.16; all p < 0.001).

TABLE 3.

The association between glycaemic/lipid biomarkers and VO₂max.

	Model 1 (unadjusted)		Model 2 (demographics adjusted)		Model 3 (fully adjusted)
	β (95% CI)	p Value	β (95% CI)	p Value	β (95% CI)	p Value
Glucose
Per SD ^a	−0.10 (−0.11, −0.10)	<0.001	−0.14 (−0.15, −0.13)	<0.001	−0.13 (−0.14, −0.11)	<0.001
Q2	−0.13 (−0.16, −0.11)	<0.001	−0.17 (−0.19, −0.14)	<0.001	−0.15 (−0.17, −0.12)	<0.001
Q3	−0.21 (−0.23, −0.18)	<0.001	−0.28 (−0.31, −0.25)	<0.001	−0.25 (−0.28, −0.22)	<0.001
Q4	−0.27 (−0.29, −0.24)	<0.001	−0.36 (−0.39, −0.33)	<0.001	−0.32 (−0.35, −0.28)	<0.001
p Trend ^b	−0.09 (−0.10, −0.08)	<0.001	−0.12 (−0.13, −0.11)	<0.001	−0.10 (−0.12, −0.09)	<0.001
Total cholesterol
Per SD ^a	−0.12 (−0.13, −0.11)	<0.001	−0.03 (−0.04, −0.02)	<0.001	−0.06 (−0.07, −0.05)	<0.001
Q2	−0.12 (−0.14, −0.10)	<0.001	−0.04 (−0.07, −0.02)	0.001	−0.07 (−0.10, −0.05)	<0.001
Q3	−0.25 (−0.27, −0.22)	<0.001	−0.07 (−0.10, −0.05)	<0.001	−0.13 (−0.16, −0.10)	<0.001
Q4	−0.32 (−0.35, −0.30)	<0.001	−0.07 (−0.10, −0.04)	<0.001	−0.15 (−0.18, −0.11)	<0.001
p Trend ^b	−0.11 (−0.12, −0.10)	<0.001	−0.02 (−0.03, −0.02)	<0.001	−0.05 (−0.06, −0.04)	<0.001
HbA1c
Per SD ^a	−0.16 (−0.17, −0.15)	<0.001	−0.18 (−0.19, −0.17)	<0.001	−0.07 (−0.08, −0.06)	<0.001
Q2	−0.18 (−0.20, −0.15)	<0.001	−0.19 (−0.22, −0.17)	<0.001	−0.07 (−0.09, −0.04)	<0.001
Q3	−0.27 (−0.30, −0.25)	<0.001	−0.32 (−0.35, −0.30)	<0.001	−0.12 (−0.15, −0.09)	<0.001
Q4	−0.43 (−0.45, −0.40)	<0.001	−0.48 (−0.51, −0.45)	<0.001	−0.19 (−0.22, −0.15)	<0.001
p Trend ^b	−0.14 (−0.14, −0.13)	<0.001	−0.16 (−0.17, −0.15)	<0.001	−0.06 (−0.07, −0.05)	<0.001
Triglycerides
Per SD ^a	−0.11 (−0.11, −0.10)	<0.001	−0.29 (−0.30, −0.28)	<0.001	−0.14 (−0.16, −0.13)	<0.001
Q2	−0.06 (−0.09, −0.04)	<0.001	−0.23 (−0.25, −0.20)	<0.001	−0.05 (−0.07, −0.02)	<0.001
Q3	−0.09 (−0.12, −0.07)	<0.001	−0.43 (−0.45, −0.40)	<0.001	−0.14 (−0.17, −0.12)	<0.001
Q4	−0.26 (−0.28, −0.23)	<0.001	−0.74 (−0.77, −0.72)	<0.001	−0.33 (−0.37, −0.30)	<0.001
p Trend ^b	−0.08 (−0.09, −0.07)	<0.001	−0.24 (−0.25, −0.23)	<0.001	−0.11 (−0.12, −0.10)	<0.001
LDL
Per SD ^a	−0.10 (−0.11, −0.09)	<0.001	−0.08 (−0.09, −0.07)	<0.001	−0.06 (−0.08, −0.05)	<0.001
Q2	−0.12 (−0.14, −0.09)	<0.001	−0.10 (−0.12, −0.07)	<0.001	−0.07 (−0.10, −0.05)	<0.001
Q3	−0.21 (−0.24, −0.19)	<0.001	−0.16 (−0.19, −0.14)	<0.001	−0.13 (−0.16, −0.10)	<0.001
Q4	−0.27 (−0.29, −0.24)	<0.001	−0.20 (−0.23, −0.17)	<0.001	−0.15 (−0.18, −0.11)	<0.001
p Trend ^b	−0.09 (−0.10, −0.08)	<0.001	−0.07 (−0.08, −0.06)	<0.001	−0.05 (−0.06, −0.04)	<0.001
HDL
Per SD ^a	−0.04 (−0.04, −0.03)	<0.001	0.27 (0.26, 0.28)	<0.001	0.07 (0.06, 0.08)	<0.001
Q2	−0.02 (−0.05, 0.00)	0.085	0.25 (0.23, 0.28)	<0.001	0.00 (−0.03, 0.02)	0.794
Q3	−0.14 (−0.16, −0.11)	<0.001	0.43 (0.41, 0.45)	<0.001	0.03 (0.01, 0.06)	0.014
Q4	−0.13 (−0.16, −0.11)	<0.001	0.69 (0.67, 0.72)	<0.001	0.13 (0.10, 0.16)	<0.001
p Trend ^b	−0.05 (−0.06, −0.04)	<0.001	0.23 (0.22, 0.23)	<0.001	0.04 (0.03, 0.05)	<0.001

Open in a new tab

Abbreviations: CI, confidence interval; HbA1c, glycated haemoglobin; SD, standard deviation.

^{^a}

The “Per SD” estimate comes from modelling the exposure as a continuous variable.

^{^b}

The “p trend” and its HR come from modelling the exposure quartiles as an ordinal variable, representing the hazard ratio per one‐quartile increase.

3.4. Subgroup analyses

Subgroup analyses stratified across demographic (sex, ethnicity and age), lifestyle (smoking status, alcohol consumption and education), clinical (BMI, self‐rated health) and biomolecular domains (CRP, T2D–PRS, BA and PhenoAge) revealed consistent protective associations between higher VO₂max and reduced incident T2D risk (Figure S2). Although the point estimates suggested a numerically greater protective effect in the low BA and low PhenoAge subgroups, no statistically significant interaction was detected. Additionally, significant interactions were observed for ethnicity (p interaction = 0.001), alcohol status (p interaction = 0.012), and overall health rating (p interaction = 0.007), with particular benefit observed in Asian and White subgroups, current drinkers and participants with better self‐reported health status.

3.5. Mediation analyses

Mediation analyses revealed significant pathway‐specific effects through biological aging mechanisms (Figure 2). BA mediated 8.34% of VO₂max's total protective effect against T2D (p < 0.001), with BA acceleration similarly mediating 8.19% (p < 0.001). PhenoAge accounted for 9.13% of the total effect (p < 0.001), while PhenoAge acceleration mediated 9.22% (p < 0.001). Notably, the average direct effect of VO₂max remained strongly significant (all p < 0.001), confirming the persistence of VO₂max protection beyond aging pathways.

Mediation of biological and phenotypic age in the association between maximal oxygen consumption (VO₂max) and type 2 diabetes mellitus (T2D). CI, confidence interval.

4. DISCUSSION

This large‐scale prospective analysis of 54 418 UK Biobank participants provides compelling evidence for a robust inverse association between VO₂max and incident T2D risk over a median follow‐up of 13.6 years. Our findings demonstrate that individuals in the highest VO₂max quartile experienced a 56% reduction in T2D risk compared to the lowest quartile, with each standard deviation increase in VO₂max conferring 28% lower risk, as shown in Table 2. Importantly, we identified biological aging mechanisms as significant mediators, accounting for approximately 8%–9% of the protective association. These results extend previous knowledge by quantifying the mediating role of BA acceleration in the fitness–diabetes relationship and confirming dose–response associations across diverse demographic and clinical subgroups.

Our findings align with and substantially extend previous epidemiological evidence linking higher cardiorespiratory fitness with reduced T2D incidence. The magnitude of risk reduction observed in our study (HR = 0.44 for highest vs. lowest quartile) is consistent with meta‐analytic estimates reporting 42% lower T2D risk among highly fit individuals. ¹⁰ Similarly, our per‐standard deviation risk reduction (28%) parallels findings from the Aerobics Center Longitudinal Study, which demonstrated 8% risk reduction per metabolic equivalent increase in fitness. ³³ However, our study advances this field by utilising objective VO₂max estimation rather than exercise testing duration or self‐reported physical activity measures, thereby minimising measurement error and enhancing precision. ³⁴

The observed dose–response relationship, confirmed through restricted cubic spline modelling, supports previous reports of monotonic protective associations. ⁷ Notably, our findings demonstrate continued risk reduction even at higher fitness levels, contrasting with some studies suggesting plateau effects. ³⁵ This discrepancy may reflect methodological differences in fitness assessment or population characteristics, emphasising the importance of objective VO₂max quantification in large‐scale epidemiological investigations.

A novel contribution of our study lies in demonstrating that biological aging mechanisms partially mediate the fitness–diabetes relationship. Both BA and PhenoAge measures mediated approximately 8%–9% of VO₂max's protective effect, providing mechanistic insight into how cardiorespiratory fitness confers metabolic protection. This finding aligns with emerging evidence that exercise training can decelerate biological aging processes through multiple pathways. ³⁶ , ³⁷

The BA measures employed in our study integrate multiple physiological systems, including cardiovascular (blood pressure), metabolic (glucose, HbA1c), inflammatory (CRP) and organ function (creatinine and albumin) markers. ¹⁹ Higher cardiorespiratory fitness likely influences these systems synergistically, resulting in a more youthful biological profile that translates into reduced T2D susceptibility. Previous interventional studies have demonstrated that structured exercise programmes can reverse BA, supporting the plausibility of our mediation findings. ³⁸

The modest proportion of total effect mediated by biological aging (8%–9%) suggests that additional mechanisms beyond aging‐related pathways contribute substantially to fitness‐diabetes protection. These likely include direct effects on insulin sensitivity through enhanced muscle glucose uptake, improved mitochondrial function and biogenesis, favourable alterations in body composition and adipose tissue distribution and beneficial changes in circulating adipokines and myokines. ³⁹ , ⁴⁰ , ⁴¹ , ⁴² Our analysis demonstrated that PhenoAge mediated 9.1% of the total effect of VO₂max on diabetes incidence. This mediating role likely arises from PhenoAge's unique capacity to capture multisystem physiological dysregulation. As an integrative biomarker of systemic aging, elevated PhenoAge reflects functional declines across multiple physiological systems—including insulin sensitivity, vascular endothelial integrity and immune regulation. The mitochondrial dysfunction and chronic low‐grade inflammation resulting from low VO₂max may drive increases in PhenoAge, thereby promoting diabetes pathogenesis through these aggregated systemic impairments. Future research incorporating comprehensive metabolomic and proteomic profiling may elucidate additional mediating pathways.

Our comprehensive analysis of glycaemic and lipid biomarkers provides mechanistic support for the observed fitness–diabetes associations. The strong inverse relationships between VO₂max and glucose, HbA1c, triglycerides and LDL cholesterol, coupled with positive associations with HDL cholesterol, reflect improved metabolic health across multiple domains. These findings are consistent with exercise physiology literature demonstrating enhanced glucose homeostasis, lipid metabolism and insulin sensitivity following aerobic training. ⁴³

The magnitude of biomarker improvements observed in our study (e.g., 0.32 standard deviations lower glucose in the highest vs. lowest VO₂max quartile) suggests clinically meaningful metabolic benefits. For context, lifestyle interventions achieving similar glucose reductions have demonstrated significant T2D prevention efficacy in randomised controlled trials. ⁴⁴

Our extensive subgroup analyses revealed consistent protective associations across diverse demographic and clinical strata, supporting the generalisability of fitness‐diabetes relationships. However, several important effect modifications emerged. The stronger protective effects observed in Asian and White ethnic groups compared to Black participants may reflect genetic variations in exercise responsiveness or baseline metabolic risk profiles. ⁴⁵ Similarly, enhanced protection among current alcohol consumers might relate to the complex metabolic interactions between moderate alcohol intake and exercise adaptations. ⁴⁶

Notably, participants with lower baseline BA and PhenoAge exhibited stronger fitness‐related protection, suggesting that individuals with more favourable aging profiles may derive greater metabolic benefits from higher cardiorespiratory fitness. This finding has important implications for personalised prevention strategies, as it suggests that maintaining both fitness and healthy aging profiles may provide synergistic protection against T2D development.

The absence of significant interactions across genetic risk strata (T2D–PRS) indicates that fitness benefits persist regardless of genetic predisposition. This finding supports universal fitness promotion strategies while highlighting that high‐risk individuals may require additional interventions beyond fitness optimisation to achieve comparable absolute risk reductions. ¹⁶

Our findings have substantial clinical and public health relevance. The robust protective associations observed across diverse subgroups support population‐wide promotion of cardiorespiratory fitness for T2D prevention. The identification of biological aging as a mediating pathway suggests that interventions targeting both fitness improvement and healthy aging may provide additive benefits.

From a clinical perspective, VO₂max assessment could serve as a valuable tool for T2D risk stratification, particularly when combined with traditional risk factors and biological aging markers. The strong associations with metabolic biomarkers suggest that fitness improvements may translate into clinically meaningful metabolic benefits detectable through routine laboratory monitoring. ⁴⁷

The dose–response relationships observed in our study support current physical activity guidelines recommending progressive fitness improvement rather than binary active/inactive classifications. ⁴⁸ However, the continued benefits observed at higher fitness levels suggest that individuals already meeting guideline recommendations may derive additional metabolic protection from further fitness enhancement.

Major strengths of our study include the large sample size, prospective design with extended follow‐up, objective VO₂max estimation, comprehensive covariate adjustment and innovative mediation analysis incorporating biological aging measures. The UK Biobank's diverse population and standardised protocols enhance the generalisability and reliability of our findings.

However, several limitations warrant consideration. First, the observational design precludes definitive causal inference, although the biological gradient, temporal sequence and mechanistic plausibility support causal relationships. Second, VO₂max estimation rather than direct measurement may introduce some imprecision, although the validated algorithm employed has demonstrated strong correlation with directly measured values. Third, BA calculations rely on available biomarkers and may not capture all aging‐related processes. Fourth, the predominantly White, middle‐aged UK Biobank population may limit generalisability to other ethnic groups and age ranges. The small sample sizes for non‐White ethnic groups preclude definitive conclusions from our ethnicity‐based subgroup analyses. These results should be interpreted as exploratory. The ethnicity‐specific findings lack the precision for reliable clinical interpretation and require validation in larger, diverse cohorts. Additionally, while our models adjust for socioeconomic status and lifestyle factors, we acknowledge that broader environmental determinants of T2D, including chronic exposure to air pollution, endocrine‐disrupting chemicals and neighbourhood‐level stressors, were not measured. ⁴⁹ The omission of these well‐established risk factors represents a gap in comprehensively evaluating T2D aetiology. We cannot exclude residual confounding from unmeasured lifestyle factors or genetic variants not captured by our PRS. The single baseline VO₂max assessment does not account for fitness changes over time, potentially underestimating the true magnitude of associations. Finally, T2D ascertainment through administrative coding may miss undiagnosed cases, although this would likely bias results towards the null.

5. CONCLUSION

This large‐scale prospective study demonstrates robust protective associations between higher cardiorespiratory fitness and reduced T2D incidence, with biological aging mechanisms partially mediating these relationships. The consistent benefits observed across diverse subgroups support population‐wide fitness promotion for T2D prevention. The identification of biological aging as a mediating pathway provides novel mechanistic insights and suggests that interventions targeting both fitness improvement and healthy aging may provide synergistic metabolic benefits. These findings support the clinical integration of fitness assessment and aging biomarkers for personalised T2D risk stratification and prevention strategies.

AUTHOR CONTRIBUTIONS

XL, XC and WY contributed to investigation, methodology, formal analysis, validation and writing—original draft. GD and YZ contributed to data curation, formal analysis, methodology and visualisation. YL, NC and JC contributed to conceptualisation, data curation, funding acquisition, investigation, project administration, resources, supervision and writing—review and editing. All authors contributed to reviewing and editing the manuscript and approved the final version to be published.

FUNDING INFORMATION

The study was supported by the Department of Science and Technology of Jilin Province (grant number: YDZJ202401227ZYTS).

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no competing interests.

ETHICS STATEMENT

Ethical approval for UK Biobank was obtained from the North West Multi‐Centre Research Ethics Committee, and written informed consent was obtained from all participants.

Supporting information

Data S1. Supporting Information.

DOM-28-914-s001.docx^{(687.7KB, docx)}

ACKNOWLEDGEMENTS

The authors declare no acknowledgement.

Liu X, Chen X, Yang W, et al. Cardiorespiratory fitness and type 2 diabetes risk: A prospective cohort study with mediation analysis of biological aging in the UK Biobank. Diabetes Obes Metab. 2026;28(2):914‐924. doi: 10.1111/dom.70261

Contributor Information

Yuguang Li, Email: 531379219@qq.com.

Naifei Chen, Email: chennaifei@jlu.edu.cn.

Jiuwei Cui, Email: cuijw@jlu.edu.cn.

DATA AVAILABILITY STATEMENT

The datasets generated and analysed during the current study are available in the UK Biobank at https://biobank.ndph.ox.ac.uk/showcase/index.cgi.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supporting Information.

DOM-28-914-s001.docx^{(687.7KB, docx)}

Data Availability Statement

The datasets generated and analysed during the current study are available in the UK Biobank at https://biobank.ndph.ox.ac.uk/showcase/index.cgi.

[dom70261-bib-0001] 1. He Q, Wu W, Chen J, et al. Global burden of type 2 diabetes in non‐elderly individuals 1990 to 2021 and projections for 2050: a systematic analysis of the 2021 global burden of disease. Diabetes Metab. 2025;51(4):101660. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0002] 2. Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol. 2018;14(2):88‐98. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0003] 3. Parker ED, Lin J, Mahoney T, et al. Economic costs of diabetes in the U.S. in 2022. Diabetes Care. 2024;47(1):26‐43. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0004] 4. Bellou V, Belbasis L, Tzoulaki I, Evangelou E. Risk factors for type 2 diabetes mellitus: an umbrella review of meta‐analyses. PLoS One. 2018;13(3):e0194127. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom70261-bib-0005] 5. Yu L, Wang J, Gong Q, et al. Influence of a diet and/or exercise intervention on long‐term mortality and vascular complications in people with impaired glucose tolerance: Da Qing diabetes prevention outcome study. Diabetes Obes Metab. 2024;26(4):1188‐1196. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0006] 6. Lee J, Stone AJ. Combined aerobic and resistance training for cardiorespiratory fitness, muscle strength, and walking capacity after stroke: a systematic review and meta‐analysis. J Stroke Cerebrovasc Dis. 2020;29(1):104498. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0007] 7. 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. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0008] 8. Imboden MT, Harber MP, Whaley MH, Finch WH, Bishop DL, Kaminsky LA. Cardiorespiratory fitness and mortality in healthy men and women. J Am Coll Cardiol. 2018;72(19):2283‐2292. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0009] 9. 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] [PubMed] [Google Scholar]

[dom70261-bib-0010] 10. 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. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0011] 11. Wei M, Gibbons LW, Mitchell TL, Kampert JB, Lee CD, Blair SN. The association between cardiorespiratory fitness and impaired fasting glucose and type 2 diabetes mellitus in men. Ann Intern Med. 1999;130(2):89‐96. [DOI] [PubMed] [Google Scholar]

[dom70261-bib-0012] 12. Hawley JA, Leckey JJ. Carbohydrate dependence during prolonged, intense endurance exercise. Sports Med. 2015;45(suppl 1):S5‐S12. [DOI] [PMC free article] [PubMed] [Google Scholar]

[dom70261-bib-0013] 13. Little JP, Safdar A, Wilkin GP, Tarnopolsky MA, Gibala MJ. A practical model of low‐volume high‐intensity interval training induces mitochondrial biogenesis in human skeletal muscle: potential mechanisms. J Physiol. 2010;588(Pt 6):1011‐1022. [DOI] [PMC free article] [PubMed] [Google Scholar]

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PERMALINK

Cardiorespiratory fitness and type 2 diabetes risk: A prospective cohort study with mediation analysis of biological aging in the UK Biobank

Xiangliang Liu, PhD

Xinqiao Chen, MM

Wang Yang, PhD

Guanran Ding, MM

Yixin Zhao, MD

Yuguang Li, ME

Naifei Chen, PhD

Jiuwei Cui, PhD

Abstract

Aims

Materials and Methods

Results

Conclusions

1. INTRODUCTION

2. METHOD

2.1. Study design and population

2.2. Assessment of VO2max

2.3. Assessment of biological age and phenotypic age

2.4. Assessment of polygenic risk score

2.5. Incident T2D

2.6. Covariates

2.7. Statistical analysis

3. RESULTS

3.1. Population characteristic

TABLE 1.

3.2. The association between VO2 max and T2D

TABLE 2.

FIGURE 1.

3.3. The association between VO2 max and glycaemic/lipid biomarkers

TABLE 3.

3.4. Subgroup analyses

3.5. Mediation analyses

FIGURE 2.

4. DISCUSSION

5. CONCLUSION

AUTHOR CONTRIBUTIONS

FUNDING INFORMATION

CONFLICT OF INTEREST STATEMENT

ETHICS STATEMENT

Supporting information

ACKNOWLEDGEMENTS

Contributor Information

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Cited by other articles

Links to NCBI Databases

2.2. Assessment of VO₂max

3.2. The association between VO₂ max and T2D

3.3. The association between VO₂ max and glycaemic/lipid biomarkers