Abstract
Objective
Ketone bodies are key metabolic intermediates with both neuroprotective and pro-oxidative properties; however, their role in glaucoma remains unclear. This study aimed to investigate associations between circulating ketone bodies and the incidence of primary open-angle glaucoma (POAG), as well as related ocular parameters, in the UK Biobank.
Design
Combined cross-sectional analysis and prospective cohort study.
Participants
A total of 456 499 participants were included in the cohort analysis; 104 225 were included in the baseline intraocular pressure (IOP) analysis, 41 495 in the baseline ganglion cell complex (GCC) thickness analysis, and 39 498 in the IOP-adjusted GCC thickness analysis.
Methods
Circulating ketone bodies were quantified using nuclear magnetic resonance–based metabolomics. Cox proportional hazards models were used to estimate hazard ratios for POAG incidence. Linear regression models assessed associations with baseline IOP and GCC thickness. Restricted cubic spline models were applied to evaluate dose–response relationships, and subgroup analyses were conducted to explore potential effect modification.
Main Outcome Measures
Incidence of POAG.
Results
During follow-up, 2330 participants developed POAG. Higher circulating ketone body levels were associated with an increased incidence of POAG after full adjustment (hazard ratio: 1.93, 95% confidence interval [CI]: 1.25–2.99, P = 0.003). Elevated ketone body levels were also associated with higher baseline IOP (β = 1.84, 95% CI: 1.57–2.10, P < 0.001) and thinner GCC both before (β = –1.62, 95% CI: –2.66 to −0.57, P = 0.002) and after IOP adjustment (β = –1.48, 95% CI: –2.53 to −0.42, P = 0.006), suggesting associations that are partially IOP-independent.
Conclusions
Elevated circulating ketone body levels were associated with a higher incidence of POAG and related ocular phenotypes. Circulating ketone body levels may serve as a potential biomarker of systemic metabolic dysregulation associated with increased POAG susceptibility.
Financial Disclosure(s)Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Keywords: Ketone bodies, Primary open-angle glaucoma, UK Biobank
Glaucoma represents a major global public health challenge and remains the leading cause of irreversible blindness worldwide.1 Among subtypes, primary open-angle glaucoma (POAG) is the most prevalent form, accounting for the majority of glaucoma cases and affecting approximately 1% to 2% of individuals aged 40 years and more.2 With the rapid aging of populations worldwide, the number of people affected by POAG is expected to increase markedly in the coming decades, further amplifying its societal and health care burden.3
Despite its high prevalence and significant impact on visual health, the biological mechanisms underlying POAG development are not fully understood.4 Current clinical and epidemiological studies have identified several established risk factors, such as elevated intraocular pressure (IOP) and increased susceptibility of the optic nerve. In addition to these traditional risk factors, emerging evidence suggests that systemic physiological conditions may also play a role in glaucoma susceptibility. For example, markers reflecting systemic health status, such as frailty as well as impaired pulmonary and renal function, have been associated with a higher likelihood of POAG.5, 6, 7, 8 These findings highlight the possibility that broader systemic processes beyond ocular factors may contribute to the pathogenesis of glaucoma.
In this context, ketone bodies have attracted increasing attention as key intermediates of systemic metabolism. The 3 principal ketone bodies, including β-hydroxybutyrate, acetoacetate, and acetone, are generated when the availability of carbohydrates is reduced, and the body shifts toward lipid utilization as a primary energy source.9 Ketone bodies are increasingly recognized not only as alternative energy substrates but also as signaling metabolites involved in cellular regulation. Experimental studies suggest that ketone bodies may exert neuroprotective effects. For example, ketone metabolism has been reported to improve mitochondrial efficiency, decrease oxidative stress, and modulate inflammatory signaling pathways, thereby contributing to metabolic regulation and neuronal protection,10 which suggest that ketone-related pathways may have therapeutic potential for inflammatory neurodegenerative conditions, including POAG. In retinal models, ketone bodies have been proven to reduce retinal ganglion cell loss and may act through preserving kynurenic acid production.11 However, evidence linking ketone metabolism to POAG is inconsistent. Epidemiological studies show no clear association between low-carbohydrate diets and POAG risk,12 and some findings suggest ketone bodies may promote oxidative stress and lipid peroxidation.13 Additionally, transcriptomic studies indicate that ketone-related epigenetic modifications may be involved in POAG pathogenesis.14
Recent population-based studies have further suggested that circulating ketone body levels are associated with adverse cardiovascular outcomes.15 Given the overlap in vascular, metabolic, and oxidative stress–related mechanisms between cardiovascular disease and POAG,15 ketone bodies may represent a biologically relevant metabolic marker in glaucoma. However, population-based evidence regarding the relationship between circulating ketone body levels and POAG remains limited. While beneficial effects have been reported in certain experimental settings, the relationship between circulating ketone bodies and POAG incidence remains uncertain and requires further investigation in population-based studies.
Methods
Study Sample
The UK Biobank is a large prospective cohort study designed to investigate the determinants of major diseases in middle-aged and older adults. Between 2006 and 2010, more than 500 000 participants were recruited from across the United Kingdom through assessment centers located in England, Scotland, and Wales. The study collected a wide range of data, including information on sociodemographic characteristics, lifestyle behaviors, medical history, physical examinations, and biological specimens. Ethical approval for the UK Biobank was granted by the North West Multi-Centre Research Ethics Committee (Ref: 21/NW/0157), and all participants provided written informed consent. The present study followed the principles outlined in the Declaration of Helsinki.
Assessment of Ketone Bodies
In this study, the main exposure of interest was circulating total ketone bodies, expressed in mmol/L. In epidemiological research, the concentrations of the 3 major ketone bodies, including β-hydroxybutyrate, acetoacetate, and acetone, are typically combined to represent total plasma ketone bodies levels.15 Baseline plasma samples from participants were used for metabolite quantification. These metabolites were measured using a high-throughput nuclear magnetic resonance metabolomics platform developed by Nightingale Health. Plasma samples were obtained from aliquot 3 and prepared in 96-well plates at the UK Biobank laboratory. The plates were then transported on dry ice to Nightingale Health laboratories in Finland in batches ranging from roughly 5000 to 20 000 samples. Upon arrival, the samples were immediately stored at –80°C until analysis. Before measurement, frozen samples were slowly thawed overnight at 4°C, gently mixed, and centrifuged to remove any precipitated material.16
Ascertainment of Outcome of Interest
In the present study, the main outcome of interest was the occurrence of POAG. To improve the specificity of case identification, POAG cases were defined using diagnostic codes from the International Classification of Diseases (ICD), including ICD-10 code H40.1 and ICD-9 code 365.1.17, 18, 19 For analyses evaluating incident POAG within the cohort, participants with any glaucoma diagnosis at baseline were excluded.20
In addition to incident POAG, baseline ocular parameters associated with POAG were also examined, including IOP and ganglion cell complex (GCC) thickness. Corneal-compensated IOP measurements were used to provide a more accurate estimate of physiological IOP by minimizing the influence of corneal properties.21 To reduce the influence of potential measurement artifacts, the highest and lowest 0.5% of IOP values were excluded.22 Participants with other forms of glaucoma were also removed from the analysis. Individuals with a history of ocular trauma, glaucoma surgery or laser treatment, corneal transplantation, or refractive laser surgery were further excluded because these conditions may alter IOP independently of its natural level.22 For participants receiving IOP-lowering medications, the measured IOP was adjusted by dividing the recorded value by 0.7, reflecting the average treatment-related reduction in IOP.22,23 When measurements from both eyes were available, the participant-level IOP was calculated as the mean of the right and left eye values; otherwise, the value from the single available eye was used.22
Within the UK Biobank cohort, a subset of participants underwent retinal imaging using OCT at baseline with the Topcon 3D OCT-1000 Mk2 system (Topcon Corp., Tokyo, Japan). To ensure image reliability, scans with a signal strength score below 45 were excluded as part of the quality control procedure.22 Additional segmentation quality indicators provided by the UK Biobank were also applied to identify unreliable scans or segmentation errors. These indicators included the inner limiting membrane indicator, validity count, minimum motion correlation, maximum motion delta, and maximum motion factor, which have been widely used and detailed described in previous studies.18,24 After excluding images with inadequate quality and participants diagnosed with other types of glaucoma at baseline, measurements of GCC thickness were obtained for analysis.
Assessment of Covariates
A range of potential confounding variables were considered in the analysis, covering demographic characteristics, socioeconomic indicators, lifestyle behaviors, clinical conditions, and medication use. Demographic factors included age, sex, and ethnicity. Socioeconomic status was assessed using the Townsend Deprivation Index, a widely used indicator of material deprivation in the United Kingdom, in which higher values correspond to greater socioeconomic disadvantage.25 Educational attainment was also included as an additional measure of socioeconomic position.
Lifestyle-related variables comprised body mass index, smoking status, alcohol consumption, dietary habits, sleep characteristics, and physical activity. Overall dietary quality was evaluated using a healthy diet score derived from 7 components based on the most recent US Dietary Guidelines, with higher scores representing healthier dietary patterns.26 Sleep health was summarized using a composite sleep score constructed from 5 sleep-related characteristics following previously established methods.27 Low-risk sleep behaviors included an early chronotype, a nightly sleep duration of 7 to 8 hours, absence or minimal symptoms of insomnia, no habitual snoring, and infrequent daytime sleepiness. Each low-risk factor contributed one point to the score, producing a total ranging from 0 to 5, with higher values indicating healthier sleep patterns. Based on this score, sleep quality was further classified into 3 categories: health (≥4), moderate (2–3), and poor (≤1).27 Physical activity levels were assessed using the International Physical Activity Questionnaire, and ideal activity was defined as meeting recommended thresholds of at least 150 minutes of moderate-intensity activity, 75 minutes of vigorous-intensity activity, or an equivalent combination totaling 150 minutes per week.28
In addition, several clinical conditions and medication exposures were included as covariates to account for potential confounding. These included physician-diagnosed diabetes, hypertension, and hyperlipidemia and the use of selected systemic medications such as glucocorticoids, statins, and calcium-channel blockers.
Statistical Analysis
Baseline characteristics of participants were first compared according to circulating ketone body levels, which were categorized into high and low groups using the median value as the cutoff for descriptive purposes only. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for the association between ketone bodies and incident POAG. Follow-up time was calculated from the date of the baseline assessment until the earliest occurrence of POAG diagnosis, death, or the end of follow-up, whichever came first. The proportional hazards assumption was evaluated using Schoenfeld residual tests. To further explore associations between ketone bodies and ocular parameters associated with POAG, linear regression models were used to examine relationships with IOP and GCC thickness. Three sequential models were constructed. Model 1 was an unadjusted model. Model 2 included adjustment for basic demographic factors (age, sex, and ethnicity). Model 3 additionally adjusted for all predefined covariates. In analyses examining GCC thickness, models were constructed both with and without additional adjustment for IOP to evaluate whether the association between ketone bodies and retinal neurodegeneration might be independent of IOP-related mechanisms.
To assess the robustness of the findings, several sensitivity analyses were performed. First, ketone body levels were categorized into quartiles to evaluate potential dose-response relationships and to examine whether the observed associations were consistent across the distribution of ketone body levels. Second, analyses were repeated after excluding participants with extreme ketone body concentrations (the highest and lowest 2.5% of the distribution) to assess the potential influence of outliers on the observed associations.
To assess potential nonlinear associations, restricted cubic spline functions were incorporated into Cox proportional hazards models and linear regression models to evaluate dose–response relationships between ketone body levels and POAG as well as ocular parameters associated with POAG, with adjustment for all covariates. Subgroup analyses were conducted according to age, sex, and ethnicity. Potential effect modification was further examined by introducing interaction terms between ketone bodies and each stratification variable into the regression models.
All statistical analyses were performed using R software (version 4.4.0; R Foundation for Statistical Computing, Vienna, Austria). All statistical tests were 2-sided, and a P value < 0.05 was considered statistically significant.
Results
Baseline Characteristics of Participants
In the cohort analysis including 456 499 participants, individuals with higher levels of ketone bodies differed significantly from those with lower levels in several baseline characteristics. Participants in the high ketone body group tended to be older, more frequently male, and predominantly of White ethnicity. They were also more likely to have a lower socioeconomic status, as reflected by higher Townsend Deprivation Index scores, and were less likely to have attained a college degree. In terms of lifestyle factors, participants with higher ketone body levels were more likely to be obese, current smokers, and alcohol drinkers and were more likely to report poorer sleep patterns. Additionally, the high ketone body group showed a higher prevalence of cardiometabolic conditions, including diabetes, hypertension, and hyperlipidemia, and were more likely to use systemic medications such as glucocorticoids, statins, and calcium-channel blockers (all P < 0.001) (Table 1).
Table 1.
Baseline Characteristics of Participants
| Variables | Ketone Bodies Level Lower than Median (n = 228 249) | Ketone Bodies Level Higher than Median (n = 228 250) | P Value |
|---|---|---|---|
| Age, n (%) | <0.001 | ||
| Middle-aged adults | 141 470 (61.98) | 120 153 (52.64) | |
| Older adults | 86 779 (38.02) | 108 097 (47.36) | |
| Sex, n (%) | <0.001 | ||
| Female | 128 305 (56.21) | 120 240 (52.68) | |
| Male | 99 944 (43.79) | 108 010 (47.32) | |
| Ethnicity, n (%) | <0.001 | ||
| Non-White | 13 504 (5.92) | 12 005 (5.26) | |
| White | 214 745 (94.08) | 216 245 (94.74) | |
| TDI, M (Q1, Q3) | –2.22 (–3.69, 0.38) | –2.07 (–3.61, 0.65) | <0.001 |
| Education, n (%) | <0.001 | ||
| College | 90 705 (39.74) | 85 610 (37.51) | |
| Other | 137 544 (60.26) | 142 640 (62.49) | |
| BMI, n (%) | <0.001 | ||
| Underweight | 1160 (0.51) | 1219 (0.53) | |
| Normal | 77 365 (33.90) | 71 689 (31.41) | |
| Overweight | 98 266 (43.05) | 95 818 (41.98) | |
| Obese | 51 458 (22.54) | 59 524 (26.08) | |
| Smoking status, n (%) | <0.001 | ||
| Never | 131 524 (57.62) | 119 018 (52.14) | |
| Previous | 75 458 (33.06) | 82 240 (36.03) | |
| Current | 21 267 (9.32) | 26 992 (11.83) | |
| Drinking status, n (%) | <0.001 | ||
| Never | 10 835 (4.75) | 9416 (4.13) | |
| Previous | 8264 (3.62) | 8282 (3.63) | |
| Current | 209 150 (91.63) | 210 552 (92.25) | |
| Healthy diet score, M (Q1, Q3) | 3.00 (2.00, 4.00) | 3.00 (2.00, 4.00) | <0.001 |
| Sleep score pattern, n (%) | <0.001 | ||
| Health | 123 734 (54.21) | 122 056 (53.47) | |
| Moderate | 97 567 (42.75) | 99 045 (43.39) | |
| Poor | 6948 (3.04) | 7149 (3.13) | |
| Ideal physical activity, n (%) | 0.566 | ||
| No | 87 283 (38.24) | 87 472 (38.32) | |
| Yes | 140 966 (61.76) | 140 778 (61.68) | |
| Diabetes, n (%) | <0.001 | ||
| No | 225 037 (98.59) | 221 913 (97.22) | |
| Yes | 3212 (1.41) | 6337 (2.78) | |
| Hypertension, n (%) | <0.001 | ||
| No | 213 207 (93.41) | 208 290 (91.26) | |
| Yes | 15 042 (6.59) | 19 960 (8.74) | |
| Hyperlipidemia, n (%) | <0.001 | ||
| No | 221 703 (97.13) | 219 654 (96.23) | |
| Yes | 6546 (2.87) | 8596 (3.77) | |
| Glucocorticoid use, n (%) | <0.001 | ||
| No | 226 032 (99.03) | 225 288 (98.70) | |
| Yes | 2217 (0.97) | 2962 (1.30) | |
| Statins use, n (%) | <0.001 | ||
| No | 196 912 (86.27) | 186 725 (81.81) | |
| Yes | 31 337 (13.73) | 41 525 (18.19) | |
| CCB use, n (%) | <0.001 | ||
| No | 215 024 (94.21) | 208 903 (91.52) | |
| Yes | 13 225 (5.79) | 19 347 (8.48) |
BMI = body mass index; CCB = calcium-channel blocker; TDI = Townsend Deprivation Index.
TDI and healthy diet scores were presented as the median (interquartile range) because they were not normally distributed according to the Kolmogorov–Smirnov test, and comparisons were performed using the Wilcoxon rank-sum test. Categorical variables were expressed as numbers (percentages) and compared using the χ2 test. Participants were categorized as middle-aged or older adults using 60 years as the age threshold. BMI (kg/m2) was classified into 4 categories: underweight (<18.5), normal weight (18.5–25), overweight (25–30), and obese (≥30).
Ketone Bodies and Primary Open-Angle Glaucoma Incidence
A total of 2330 participants developed incident POAG during the follow-up period. The baseline ketone body level in the study population had a median value of 0.067 mmol/L, with an interquartile range of 0.051 to 0.099 mmol/L (Supplementary Figure S1, available at https://www.ophthalmologyscience.org). The median follow-up duration was 13.67 years (interquartile range: 12.92‑14.38 years). In the unadjusted model, higher ketone body levels were associated with a significantly increased incidence of POAG (HR: 2.50, 95% CI: 1.74‑3.61, P < 0.001). After adjustment for demographic variables, the association remained significant, although the magnitude of the effect was attenuated (HR: 2.04, 95% CI: 1.33‑3.13, P = 0.001). Further adjustment for all covariates yielded similar results (HR: 1.93, 95% CI: 1.25‑2.99, P = 0.003) (Table 2). The proportional hazards assumption was satisfied (total ketone bodies: χ2 = 0.024, degrees of freedom = 1, P = 0.877; global test: χ2 = 23.82, degrees of freedom = 23, P = 0.414). When ketone body levels were categorized into quartiles, participants in the highest quartile exhibited a numerically higher risk of incident POAG compared with those in the lowest quartile, although the association did not reach statistical significance (P for trend = 0.100). After excluding participants with ketone body concentrations in the highest and lowest 2.5% of the distribution, the association between ketone body levels and incident POAG remained directionally consistent but was no longer statistically significant (HR = 1.73, 95% CI: 0.75‑4.00, P = 0.198) (Supplementary Tables S1 and S2, available at https://www.ophthalmologyscience.org).
Table 2.
Association between Circulating Ketone Bodies and POAG Incidence
| Outcome | Model 1 |
Model 2 |
Model 3 |
|||
|---|---|---|---|---|---|---|
| HR (95% CI) | P value | HR (95% CI) | P value | HR (95% CI) | P value | |
| POAG incidence | 2.50 (1.74-3.61) | <0.001 | 2.04 (1.33-3.13) | 0.001 | 1.93 (1.25-2.99) | 0.003 |
CI = confidence interval; HR = hazard ratio; POAG = primary open-angle glaucoma.
The model 1 was raw. The model 2 was adjusted for age, sex, and ethnicity. The model 3 was adjusted for age, sex, ethnicity, Townsend Deprivation Index, education level, body mass index, smoking status, drinking status, healthy diet score, sleep score pattern, physical activity, diabetes, hypertension, hyperlipidemia, and systemic medication use (glucocorticoids, statins, and calcium-channel blockers).
Restricted cubic spline analysis showed no evidence of a nonlinear association between ketone body levels and POAG incidence after adjustment for covariates (P for nonlinearity = 0.912) (Fig 1). In addition, subgroup analyses revealed no significant interaction by age (P for interaction = 0.545), sex (P for interaction = 0.867), or ethnicity (P for interaction = 0.582), suggesting that the observed association was generally consistent across different demographic groups (Table 3).
Figure 1.
Restricted cubic spline analysis of circulating ketone bodies in relation to the incidence of primary open-angle glaucoma, baseline intraocular pressure, and ganglion cell complex thickness. CI = confidence interval; GCC = ganglion cell complex; IOP = intraocular pressure; RCS = restricted cubic spline.
Table 3.
Subgroup Analysis between Circulating Ketone Bodies and POAG and Related Traits
| Variables | Effect (95% CI) | P Value | P for Interaction |
|---|---|---|---|
| POAG incidence | |||
| Age | 0.545 | ||
| Middle-aged adults | 2.27 (1.12-4.62) | 0.023 | |
| Older adults | 1.72 (1.00-2.95) | 0.049 | |
| Sex | 0.867 | ||
| Female | 1.88 (1.01-3.51) | 0.047 | |
| Male | 2.01 (1.11-3.65) | 0.022 | |
| Ethnicity | 0.582 | ||
| Non-White | 1.35 (0.22-8.47) | 0.748 | |
| White | 2.02 (1.29-3.15) | 0.002 | |
| IOP | |||
| Age | <0.001 | ||
| Middle-aged adults | 1.34 (0.99-1.69) | < 0.001 | |
| Older adults | 2.38 (1.98-2.79) | < 0.001 | |
| Sex | 0.450 | ||
| Female | 1.93 (1.58-2.28) | < 0.001 | |
| Male | 1.73 (1.33-2.14) | < 0.001 | |
| Ethnicity | 0.889 | ||
| Non-White | 1.97 (0.99-2.96) | < 0.001 | |
| White | 1.84 (1.56-2.11) | < 0.001 | |
| GCC | |||
| Age | 0.388 | ||
| Middle-aged adults | –1.97 (–3.31 to –0.64) | 0.004 | |
| Older adults | –1.01 (–2.67 to 0.66) | 0.236 | |
| Sex | 0.192 | ||
| Female | –1.07 (–2.49 to 0.34) | 0.137 | |
| Male | –2.18 (–3.71 to –0.64) | 0.006 | |
| Ethnicity | 0.926 | ||
| Non-White | –1.11 (–5.58 to 3.36) | 0.626 | |
| White | –1.63 (–2.70 to –0.56) | 0.003 | |
CI = confidence interval; GCC = ganglion cell complex; IOP = intraocular pressure; POAG = primary open-angle glaucoma.
All results were adjusted for age, sex, ethnicity, Townsend Deprivation Index, education level, body mass index, smoking status, drinking status, healthy diet score, sleep score pattern, physical activity, diabetes, hypertension, hyperlipidemia, and systemic medication use (glucocorticoids, statins, and calcium-channel blockers).
Ketone Bodies and Ocular Parameters Associated with Primary Open-Angle Glaucoma
A total of 104 225 participants were included in the analysis of baseline IOP, 41 495 in the analysis of baseline GCC thickness, and 39 498 in the analysis of IOP-adjusted GCC thickness. In the unadjusted model, higher ketone body levels were associated with higher IOP (β = 2.15, 95% CI: 1.88‑2.42, P < 0.001) and thinner GCC, both without IOP adjustment (β = –2.67, 95% CI: –3.72 to −1.61, P < 0.001) and after additional adjustment for IOP (β = –2.36, 95% CI: –3.42 to −1.29, P < 0.001). After adjustment for demographic variables, the associations remained significant but were attenuated. Higher ketone body levels were associated with increased IOP (β = 1.77, 95% CI: 1.50‑2.04, P < 0.001) and reduced GCC thickness, both without IOP adjustment (β = –1.67, 95% CI: –2.71 to −0.62, P = 0.002) and with IOP adjustment (β = –1.53, 95% CI: –2.59 to −0.48, P = 0.004). After further adjustment for all covariates, the associations persisted, with higher ketone body levels associated with higher IOP (β = 1.84, 95% CI: 1.57‑2.10, P < 0.001) and thinner GCC, both without IOP adjustment (β = –1.62, 95% CI: –2.66 to −0.57, P = 0.002) and with additional IOP adjustment (β = –1.48, 95% CI: –2.53 to −0.42, P = 0.006) (Table 4).
Table 4.
Association between Circulating Ketone Bodies and POAG-Related Traits
| POAG-Related Traits | Model 1 |
Model 2 |
Model 3 |
|||
|---|---|---|---|---|---|---|
| β (95% CI) | P Value | β (95% CI) | P Value | β (95% CI) | P Value | |
| IOP | 2.15 (1.88–2.42) | <0.001 | 1.77 (1.50–2.04) | <0.001 | 1.84 (1.57–2.10) | <0.001 |
| GCC | –2.67 (–3.72 to –1.61) | <0.001 | –1.67 (–2.71 to –0.62) | 0.002 | –1.62 (–2.66 to –0.57) | 0.002 |
| GCC (IOP-adjusted) | –2.36 (–3.42 to –1.29) | <0.001 | –1.53 (–2.59 to –0.48) | 0.004 | –1.48 (–2.53 to –0.42) | 0.006 |
CI = confidence interval; GCC = ganglion cell complex; IOP = intraocular pressure; POAG = primary open-angle glaucoma.
The model 1 was raw. The model 2 was adjusted for age, sex, and ethnicity. The model 3 was adjusted for age, sex, ethnicity, Townsend Deprivation Index, education level, body mass index, smoking status, drinking status, healthy diet score, sleep score pattern, physical activity, diabetes, hypertension, hyperlipidemia, and systemic medication use (glucocorticoids, statins, and calcium-channel blockers).
Sensitivity analyses supported the robustness of the associations between ketone body levels and POAG-related ocular parameters. In quartile-based analyses, higher ketone body quartiles were associated with progressively higher IOP (P for trend < 0.001) and lower GCC thickness (P for trend < 0.001). Furthermore, after excluding participants with ketone body concentrations in the highest and lowest 2.5% of the distribution, the associations remained statistically significant, with higher ketone body levels associated with increased IOP (β = 2.68, 95% CI: 2.26–3.09, P < 0.001) and reduced GCC thickness (β = –2.57, 95% CI: –4.27 to −0.87, P = 0.003) (Supplementary Tables S1 and S2).
Restricted cubic spline analysis showed no evidence of a nonlinear association between ketone body levels and GCC thickness after adjustment for covariates (P for nonlinearity = 0.060). In contrast, a nonlinear association was observed between ketone body levels and IOP (P for nonlinearity < 0.001) (Fig 1). Subgroup analyses showed no significant interaction by age (P for interaction = 0.388), sex (P for interaction = 0.192), or ethnicity (P for interaction = 0.926) for GCC thickness, indicating that the association was generally consistent across demographic groups (Table 3). For IOP, a significant interaction with age was observed (P for interaction < 0.001), with a stronger association among older adults (β = 2.38, 95% CI: 1.98‑2.79, P < 0.001) compared with middle-aged adults (β = 1.34, 95% CI: 0.99‑1.69, P < 0.001). No significant interaction was detected for sex (P for interaction = 0.450) or ethnicity (P for interaction = 0.889) (Table 3).
Discussion
In this large population-based cohort study from the UK Biobank, higher circulating ketone body levels were associated with an increased POAG incidence. Elevated ketone body levels were also associated with higher baseline IOP and thinner GCC thickness. Notably, the association with GCC thickness remained significant after additional adjustment for IOP, suggesting that circulating ketone body levels may serve as a biomarker of biological processes related to retinal neurodegeneration beyond IOP-related pathways. Although the association with incident POAG was attenuated in sensitivity analyses, its direction remained consistent with the primary findings, whereas the associations with IOP and GCC thickness were consistently replicated. Given that POAG represents a downstream clinical endpoint that develops following cumulative structural and functional changes, whereas elevated IOP and GCC thinning are earlier disease-related phenotypes, these findings may indicate that circulating ketone body levels are more directly associated with early glaucoma-related alterations than with clinically detectable POAG itself. Besides, the observed differences in GCC thickness were modest in absolute magnitude and may have limited clinical significance at the individual patient level; they may nevertheless reflect subtle neurodegenerative changes at the population level. These findings provide population-based evidence linking circulating ketone bodies to POAG incidence and ocular parameters associated with POAG.
Several experimental studies have investigated the effects of ketone bodies on oxidative stress and cellular metabolism, but the findings remain inconsistent. Some studies have reported that ketone body metabolism may reduce reactive oxygen species (ROS) production,29 whereas others have observed increased oxidative stress30 or no significant change.31 Among studies suggesting reduced ROS production, it has been proposed that ketone body metabolism may decrease the Q semiquinone, thereby lowering ROS generation.32 In contrast, other experimental studies have reported pro-oxidant effects of ketone bodies. For example, treatment with different concentrations of ketone bodies for 24 hours was shown to increase oxidizing species and reduce antioxidant defenses.33 Elevated ketone body levels have also been associated with lipid peroxidation, which is promoted by oxygen radicals.13 Additional mechanistic evidence suggests that ketone bodies may increase oxidative stress through upregulation of NADPH oxidase 4.34
However, further studies have suggested a more complex pattern. In animal experiments involving ketogenic exposure, ROS levels increased during the early stage of the diet but decreased after longer periods of exposure.35 Similarly, ketone bodies have been shown to induce moderate oxidative stress that activates the transcription factor Nrf2, which subsequently promotes the transcription of antioxidant response genes such as HO-1.36 This response may enhance cellular resistance to subsequent stress. Nevertheless, it should be noted that the activation of Nrf2 does not necessarily indicate a protective state, as Nrf2 upregulation may reflect an impaired oxidative cellular environment and could potentially have harmful long-term effects.36 Therefore, previous research has suggested that the biological mechanisms associated with reduced inflammation and oxidative stress may evolve with prolonged exposure to ketogenic metabolism, and the neuroprotective effects observed in short-term settings may not necessarily translate into long-term outcomes.37
Besides, findings from animal experiments do not always translate directly to human populations. For example, although ketone bodies were reported to improve motor function in rat models of Parkinson disease, similar benefits were not observed in clinical studies of patients with Parkinson disease.37 These discrepancies highlight the need for caution when extrapolating experimental findings from animal models to human conditions. Moreover, many of the neuroprotective mechanisms proposed for ketone bodies have been investigated in specific experimental models, and their applicability to other forms of neuronal injury remains uncertain. The biological effects of ketone bodies may also vary across different tissues. For instance, their influence on antioxidant systems has been reported to differ between tissue types, which may partly contribute to the heterogeneous findings reported in the literature.38 Our findings suggest that elevated circulating ketone body levels may be linked to POAG susceptibility in the general population. Previous clinical evidence also suggests a potential link between circulating ketone bodies and glaucoma progression. Higher plasma ketone body levels have been associated with faster progression of central visual field loss,39 which is consistent with the associations observed in the present study. Age-related differences in ketone body responses have also been reported, which may help explain the age-related interaction observed for IOP in our study.40
Numerous experimental studies have reported neuroprotective effects of ketone bodies or ketogenic interventions, including improved mitochondrial function, reduced inflammation, enhanced metabolic resilience, and protection of retinal ganglion cells.10,11 However, these studies generally evaluate the effects of controlled exogenous ketone exposure or ketogenic interventions under specific experimental conditions. In contrast, circulating ketone body levels measured in population-based cohorts may reflect underlying metabolic states rather than the direct biological effects of ketone bodies themselves. Elevated endogenous ketone body levels can arise from a variety of physiological and pathological conditions, including diabetes, fasting, systemic illness, and other forms of metabolic stress. Therefore, higher circulating ketone body levels in observational studies may serve as markers of broader metabolic dysregulation rather than indicators of a beneficial ketogenic state. This distinction may partly explain why neuroprotective effects observed in experimental settings do not necessarily translate into favorable associations in epidemiological studies. Participants with higher ketone body levels exhibited a less favorable metabolic profile, including higher prevalences of obesity, diabetes, hypertension, and hyperlipidemia. Although extensive adjustment for these factors was performed, residual confounding related to the severity or complexity of metabolic dysfunction cannot be completely excluded. Therefore, circulating ketone body levels may act, at least in part, as an integrated biomarker of metabolic stress associated with increased susceptibility to POAG and related ocular traits.
This study has several strengths, including the large population-based cohort design, prospective follow-up, standardized measurement of circulating ketone bodies, and the simultaneous evaluation of multiple glaucoma-related outcomes, including POAG incidence, IOP, and GCC thickness. However, some limitations should be considered when interpreting the findings. First, as an observational analysis, the study cannot completely eliminate the possibility of residual confounding. Although a wide range of covariates were included in the models, unmeasured or unknown factors may still have influenced the observed associations. Another limitation is that elevated circulating ketone body levels may arise from heterogeneous physiological and pathological conditions, including diabetes, fasting, systemic illness, alcohol-related ketosis, and dietary carbohydrate restriction. The UK Biobank data did not allow us to distinguish these underlying causes in detail. Consequently, the observed associations should be interpreted as relating to circulating ketone body levels themselves rather than to any specific source of ketosis. Furthermore, elevated circulating ketone body levels may, at least in part, reflect broader systemic metabolic dysregulation rather than an isolated biological exposure. Therefore, the present findings should be interpreted as evidence of an epidemiological association rather than proof of a causal role of ketone body metabolism in POAG development. In addition, circulating ketone body levels were measured only once at baseline, and repeated measurements were limited. Consequently, we were unable to evaluate within-individual changes in ketone body levels over time or examine whether longitudinal variations in ketone body levels are associated with subsequent POAG incidence. Future research with repeated assessments of ketone bodies will be important to determine whether temporal patterns of ketone metabolism provide additional value for risk prediction.
Conclusions
Higher circulating ketone body levels were associated with increased POAG incidence and ocular parameters associated with POAG, including higher baseline IOP and thinner GCC thickness. Circulating ketone body levels may represent a biomarker of systemic metabolic dysregulation associated with POAG and related ocular traits. However, it is important to emphasize that the present findings should not be interpreted as evidence that such dietary interventions increase the risk of POAG. Further studies specifically designed to evaluate dietary ketosis and glaucoma-related outcomes are warranted.
Acknowledgments
This research has been conducted using the UK Biobank Resource under application number 95829.
Manuscript no. XOPS-D-26-00552.
Footnotes
Supplemental material available at www.ophthalmologyscience.org.
Disclosure(s):
All authors have completed and submitted the ICMJE disclosures form.
The authors made the following disclosures:
L.L.: All support for the present manuscript ‑ National Natural Science Foundation of China (8217040283).
S.D.: All support for the present manuscript ‑ Guangdong Basic and Applied Basic Research Foundation (2024A1515140098).
Ye.Z.: All support for the present manuscript ‑ National Natural Science Foundation of China (82471074).
Yi.Z.: All support for the present manuscript ‑ Guangdong Basic and Applied Basic Research Foundation (2024A1515140098, 2024A1515013058).
This study was supported by the National Natural Science Foundation of China (8217040283, 82471074) and the Guangdong Basic and Applied Basic Research Foundation (2024A1515140098, 2024A1515013058). The funding bodies had no role in the design of the study; in the collection, analysis, or interpretation of the data; or in writing the manuscript.
HUMAN SUBJECTS: Human subjects were included in this study. The study collected a wide range of data, including information on sociodemographic characteristics, lifestyle behaviors, medical history, physical examinations, and biological specimens. Ethical approval for the UK Biobank was granted by the North West Multi-Centre Research Ethics Committee (Ref: 21/NW/0157), and all participants provided written informed consent. The present study followed the principles outlined in the Declaration of Helsinki.
No animal subjects were used in this study.
Author Contributions:
Conception and design: Chen, Wang, Deng, Yu, Lei, Zhuo, Zhu
Analysis and interpretation: Chen, Wang, Deng, Li, Chen, Lei, Zhuo, Zhu
Data collection: Chen, Zhuo
Overall responsibility: Chen, Wang, Deng, Li, Chen, Liang, Li, Yu, Lei, Zhuo, Zhu
Obtained funding: Deng, Lei, Zhuo, Zhu
Contributor Information
Lei Lei, Email: leilei25@mail.sysu.edu.cn.
Yehong Zhuo, Email: zhuoyh@mail.sysu.edu.cn.
Yingting Zhu, Email: zhuyt35@mail.sysu.edu.cn.
Supplementary Data
References
- 1.Pascolini D., Mariotti S.P. Global estimates of visual impairment: 2010. Br J Ophthalmol. 2012;96:614–618. doi: 10.1136/bjophthalmol-2011-300539. [DOI] [PubMed] [Google Scholar]
- 2.Liu T., Xie L., Ye J., He X. Family-based analysis identified CD2 as a susceptibility gene for primary open angle glaucoma in Chinese Han population. J Cell Mol Med. 2014;18(4):600–609. doi: 10.1111/jcmm.12201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Chen J., Zhu Y., Li Z., et al. Global impact of population aging on vision loss prevalence: a population-based study. Glob Transitions. 2024;6:28–36. [Google Scholar]
- 4.Wang W., Moroi S., Bakulski K., et al. Bone lead levels and risk of incident primary open-angle glaucoma: the VA Normative Aging Study. Environ Health Perspect. 2018;126 doi: 10.1289/EHP3442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chen J., Cao X., Zhuo X., et al. Relationships between frailty and the risk of glaucoma in middle-aged and older adults. Ophthalmol Glaucoma. 2024 doi: 10.1016/j.ogla.2024.09.006. S2589-4196(24)00178-00179 [pii] [DOI] [PubMed] [Google Scholar]
- 6.Liu W., Guo R., Wang S., et al. Association between chronic kidney disease and glaucoma: results from the Lifelines Cohort Study and UK Biobank. Invest Ophthalmol Vis Sci. 2025;66(15):17. doi: 10.1167/iovs.66.15.17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Yu J., Zhang Y., Kam K.W., et al. Lung function as a biomarker for glaucoma: the UK Biobank Study. Invest Ophthalmol Vis Sci. 2025;66(2):48. doi: 10.1167/iovs.66.2.48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Jonas J.B., Aung T., Bourne R.R., et al. Glaucoma. Lancet. 2017;390:2183–2193. doi: 10.1016/S0140-6736(17)31469-1. [DOI] [PubMed] [Google Scholar]
- 9.Puchalska P., Crawford P.A. Multi-dimensional roles of ketone bodies in fuel metabolism, signaling, and therapeutics. Cell Metab. 2017;25(2):262–284. doi: 10.1016/j.cmet.2016.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Pali D.V., Kim S., Mantik K., et al. Unraveling the translational relevance of β-hydroxybutyrate as an intermediate metabolite and signaling molecule. Int J Mol Sci. 2025;26(15):7362. doi: 10.3390/ijms26157362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Thaler S., Choragiewicz T.J., Rejdak R., et al. Neuroprotection by acetoacetate and β-hydroxybutyrate against NMDA-induced RGC damage in rat--possible involvement of kynurenic acid. Graefes Arch Clin Exp Ophthalmol. 2010;248(12):1729–1735. doi: 10.1007/s00417-010-1425-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hanyuda A., Rosner B.A., Wiggs J.L., et al. Low-carbohydrate-diet scores and the risk of primary open-angle glaucoma: data from three US cohorts. Eye (Lond) 2020;34(8):1465–1475. doi: 10.1038/s41433-020-0820-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Jain S.K., Kannan K., Lim G. Ketosis (acetoacetate) can generate oxygen radicals and cause increased lipid peroxidation and growth inhibition in human endothelial cells. Free Radic Biol Med. 1998;25(9):1083–1088. doi: 10.1016/s0891-5849(98)00140-3. [DOI] [PubMed] [Google Scholar]
- 14.Dai Q., Zhao S., Li J., et al. Integration of single-cell and bulk transcriptomics reveals β-hydroxybutyrylation-related signatures in primary open-angle glaucoma. Exp Eye Res. 2025;254 doi: 10.1016/j.exer.2025.110272. [DOI] [PubMed] [Google Scholar]
- 15.Chevli P.A., Mirzai S., Kazibwe R., et al. Circulating ketone bodies and incident cardiovascular outcomes and mortality: insights from the UK Biobank. J Am Heart Assoc. 2026;15(5) doi: 10.1161/JAHA.125.042582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Julkunen H., Cichońska A., Tiainen M., et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat Commun. 2023;14(1):604. doi: 10.1038/s41467-023-36231-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Chen J., Ling Y., Zhu Y., et al. Early life body size trajectory and the risk of primary open-angle glaucoma: a retrospective cohort study. Am J Ophthalmol. 2025;279:264–273. doi: 10.1016/j.ajo.2025.07.039. [DOI] [PubMed] [Google Scholar]
- 18.Chen J., Huang S., Zhuo X., et al. Association between outdoor daylight exposure duration and primary open-angle glaucoma. Am J Ophthalmol. 2025;281:162–171. doi: 10.1016/j.ajo.2025.09.022. [DOI] [PubMed] [Google Scholar]
- 19.Chen J., Xiao Y., Chen X., et al. Association between body composition and risk of primary open-angle glaucoma. Am J Ophthalmol. 2025 doi: 10.1016/j.ajo.2025.12.014. S0002-9394(25)00664-00666 [pii] [DOI] [PubMed] [Google Scholar]
- 20.Chen J., Zhu Y., Huang S., et al. Association between chronic pain and risk of glaucoma: a prospective cohort study. Transl Vis Sci Technol. 2025;14(12):24. doi: 10.1167/tvst.14.12.24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Chen J., Cao X., Chen X., et al. Causal relationship between central corneal thickness and open-angle glaucoma: evidence from Mendelian randomization. Exp Eye Res. 2024;246 doi: 10.1016/j.exer.2024.110000. [DOI] [PubMed] [Google Scholar]
- 22.Kastner A., Stuart K.V., Montesano G., et al. Calcium channel blocker use and associated glaucoma and related traits among UK Biobank participants. JAMA Ophthalmol. 2023;141(10):956–964. doi: 10.1001/jamaophthalmol.2023.3877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.van der Valk R., Webers C.A., Schouten J.S., et al. Intraocular pressure-lowering effects of all commonly used glaucoma drugs: a meta-analysis of randomized clinical trials. Ophthalmology. 2005;112(7):1177–1185. doi: 10.1016/j.ophtha.2005.01.042. [DOI] [PubMed] [Google Scholar]
- 24.Keane P.A., Grossi C.M., Foster P.J., et al. Optical coherence tomography in the UK Biobank Study - rapid automated analysis of retinal thickness for large population-based studies. PLoS One. 2016;11 doi: 10.1371/journal.pone.0164095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Ye J., Wen Y., Sun X., et al. Socioeconomic deprivation index is associated with psychiatric disorders: an observational and genome-wide gene-by-environment interaction analysis in the UK Biobank cohort. Biol Psychiatry. 2021;89:888–895. doi: 10.1016/j.biopsych.2020.11.019. [DOI] [PubMed] [Google Scholar]
- 26.Luo P., Ying J., Li J., et al. Air pollution and allergic rhinitis: findings from a prospective cohort Study. Environ Sci Technol. 2023;57(42):15835–15845. doi: 10.1021/acs.est.3c04527. [DOI] [PubMed] [Google Scholar]
- 27.Fan M., Sun D., Zhou T., et al. Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants. Eur Heart J. 2020;41(11):1182–1189. doi: 10.1093/eurheartj/ehz849. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lloyd-Jones D.M., Hong Y., Labarthe D., et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation. 2010;121:586–613. doi: 10.1161/CIRCULATIONAHA.109.192703. [DOI] [PubMed] [Google Scholar]
- 29.Stafford P., Abdelwahab M.G., Kim D.Y., et al. The ketogenic diet reverses gene expression patterns and reduces reactive oxygen species levels when used as an adjuvant therapy for glioma. Nutr Metab (Lond) 2010;7:74. doi: 10.1186/1743-7075-7-74. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Allen B.G., Bhatia S.K., Buatti J.M., et al. Ketogenic diets enhance oxidative stress and radio-chemo-therapy responses in lung cancer xenografts. Clin Cancer Res. 2013;19(14):3905–3913. doi: 10.1158/1078-0432.CCR-12-0287. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Poff A.M., Ward N., Seyfried T.N., et al. Non-toxic metabolic management of metastatic cancer in VM mice: novel combination of ketogenic diet, ketone supplementation, and hyperbaric oxygen therapy. PLoS One. 2015;10(6) doi: 10.1371/journal.pone.0127407. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Veech R.L., Chance B., Kashiwaya Y., et al. Ketone bodies, potential therapeutic uses. IUBMB Life. 2001;51(4):241–247. doi: 10.1080/152165401753311780. [DOI] [PubMed] [Google Scholar]
- 33.Shi X., Li X., Li D., et al. β-Hydroxybutyrate activates the NF-κB signaling pathway to promote the expression of pro-inflammatory factors in calf hepatocytes. Cell Physiol Biochem. 2014;33(4):920–932. doi: 10.1159/000358664. [DOI] [PubMed] [Google Scholar]
- 34.Kanikarla-Marie P., Jain S.K. Hyperketonemia and ketosis increase the risk of complications in type 1 diabetes. Free Radic Biol Med. 2016;95:268–277. doi: 10.1016/j.freeradbiomed.2016.03.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Milder J.B., Liang L.P., Patel M. Acute oxidative stress and systemic Nrf2 activation by the ketogenic diet. Neurobiol Dis. 2010;40(1):238–244. doi: 10.1016/j.nbd.2010.05.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Meroni E., Papini N., Criscuoli F., et al. Metabolic responses in endothelial cells following exposure to ketone bodies. Nutrients. 2018;10(2):250. doi: 10.3390/nu10020250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Gough S.M., Casella A., Ortega K.J., Hackam A.S. Neuroprotection by the ketogenic diet: evidence and controversies. Front Nutr. 2021;8 doi: 10.3389/fnut.2021.782657. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Ziegler D.R., Ribeiro L.C., Hagenn M., et al. Ketogenic diet increases glutathione peroxidase activity in rat hippocampus. Neurochem Res. 2003;28(12):1793–1797. doi: 10.1023/a:1026107405399. [DOI] [PubMed] [Google Scholar]
- 39.Hanyuda A., Raita Y., Ninomiya T., et al. Metabolomic profiling of open-angle glaucoma etiologic endotypes: Tohoku Multi-Omics Glaucoma Study. Invest Ophthalmol Vis Sci. 2024;65(13):44. doi: 10.1167/iovs.65.13.44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Zarnowski T., Choragiewicz T.J., Schuettauf F., et al. Ketogenic diet attenuates NMDA-induced damage to rat's retinal ganglion cells in an age-dependent manner. Ophthalmic Res. 2015;53(3):162–167. doi: 10.1159/000379753. [DOI] [PubMed] [Google Scholar]
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