Interaction between gender and age on the methylation levels of KLF14 promoter

Abstract

This study investigated the relationship between age, sex, and methylation levels of the Krüppel-like factor 14 (KLF14) promoter. Utilizing data from the Taiwan Biobank (TWB), established in 2005, we analyzed a cohort of 1,141 participants. DNA methylation analyses were conducted by Health GeneTech Corp., commissioned by TWB. Methylation levels of the KLF14 promoter were determined by averaging the values of 10 CpG sites: cg09823095, cg25109431, cg21449170, cg05651960, cg18751682, cg08097417, cg07955995, cg22285878, cg21520933, and cg06533629. Participants were divided into four age groups (30–40, 40–50, 50–60, and 60–70 years). Multiple linear regression analyses were performed to assess the impact of age and sex on KLF14 promoter methylation and to evaluate the interaction between these variables. Our findings revealed that men exhibited higher KLF14 promoter methylation levels, with a significant age-dependent increase. Notably, in participants aged over 50, KLF14 methylation levels were substantially higher in men compared to women (β = 0.00336, P = 0.0108 for the 50–60 age group; β = 0.00472, P = 0.0238 for the 60–70 age group). Our study reveals a sex-specific increase in KLF14 promoter methylation among men over 50, with no significant differences observed in individuals younger than 50. This finding suggests that age and sex may play a crucial role in the epigenetic regulation of KLF14.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-01536-8.

Introduction

The regulation of gene expression through DNA methylation is a critical mechanism that influences numerous biological processes, including development, aging, and disease susceptibility. Among the genes implicated in metabolic regulation and obesity, KLF14 (Krüppel-like factor 14) has garnered attention due to its role in adipocyte differentiation and insulin sensitivity¹. Recent studies have suggested that variations in the methylation levels of the KLF14 promoter may be associated with metabolic disorders in diverse populations^1,2.

Age is a well-established factor influencing DNA methylation patterns, with studies indicating that methylation levels tend to change in a tissue-specific manner as individuals age. For instance, it has been shown that age-related changes in DNA methylation are not uniform across the genome but rather occur at specific CpG sites, leading to alterations in gene expression that can impact various biological processes, including metabolism and aging^3,4. Moreover, the accumulation of methylation errors over time has been associated with age-related diseases, suggesting that understanding these patterns could provide insights into the biological aging process^5,6. Previous research⁷ indicated that many age-related epigenetic changes are characterized by a combination of programmed alterations and those resulting from cumulative environmental exposures and random errors. Notably, KLF14 has been identified as a gene that can serve as an epigenetic biomarker of aging, with its promoter showing signs of hypermethylation that correlate with chronological age as indicated by Akash et al., 2023 ⁸. This suggests that KLF14 dysregulation may occur in tandem with other epigenetic modifications that collectively influence age-related diseases, including metabolic disorders and cognitive decline.

Sex differences also play a significant role in shaping DNA methylation patterns⁸. Distinct methylation profiles may be shaped by hormonal variations, environmental exposures, and lifestyle factors. Additionally, sex and age differences in metabolic processes and the prevalence of obesity-related conditions highlight the need for a nuanced examination of how methylation may vary across demographic groups⁹.

The interaction between age and sex regarding KLF14 promoter methylation is particularly relevant in the context of metabolic health. Research indicates that the methylation status of KLF14 may differ significantly between males and females, especially as they age, potentially leading to variations in metabolic health outcomes^9,10. For example, hypermethylation of the KLF14 promoter has been associated with adverse metabolic traits, and this relationship may be modulated by age and sex^1,10.

KLF14 has become an essential metabolic transcriptional regulator across multiple organs¹¹. It belongs to the Krüppel-like factor family, a group of transcription factors characterized by their zinc-finger motifs that can interact with GC-rich DNA sequences. It has been proposed that increasing KLF14 expression in adipose tissue may offer metabolic advantages, particularly in women¹². Furthermore, the authors recommended further investigations into the mechanisms behind KLF14’s effects on adipose tissue to fully assess its viability as a therapeutic target. KLF14 serves as a key trans-regulatory gene that plays a crucial role in managing various biological functions and cellular pathways¹³. It influences several vital processes, such as lipid metabolism, insulin secretion, and the regulation of glucose levels.

Research has indicated that KLF14 methylation is significantly associated with metabolic traits predominantly in female participants, suggesting that age-related changes in methylation may have different implications for men and women⁹. By investigating the interactions between sex, age, and KLF14 promoter methylation, this study aims to contribute to the literature on epigenetics and its implications for health outcomes. The findings could provide insights into personalized approaches for preventing and managing metabolic diseases, underscoring the importance of considering demographic factors in epigenetic research.

This study explores the potential interplay between sex and age on the methylation levels of the KLF14 promoter. Understanding how these factors contribute to epigenetic modifications is essential for elucidating the underlying mechanisms that drive metabolic health and disease. By investigating the interactions between these variables, this study aims to contribute to the growing body of literature on epigenetics and its implications for health outcomes. The findings could provide insights into personalized approaches for preventing and managing metabolic diseases, underscoring the importance of considering demographic factors in epigenetic research.

Methods

Data resource and study participants

Data were obtained from the TWB, established in 2005 to investigate the factors contributing to disease and their underlying mechanisms. The TWB comprises a diverse cohort of over 200,000 participants, aged 30 to 70 years, who are recruited from various regions across Taiwan. Participants are selected to represent the general population. This population encompasses individuals with Han Chinese ancestry and indigenous Taiwanese groups. Before registration, each participant signed an informed consent form. Data collection in the biobank was conducted in three phases, which included questionnaires and physical and biochemical examinations. We analyzed various components in the TWB which included genetic data, demographic information (such as sex, age, BMI, and waist-hip ratio), and lifestyle factors (such as tobacco use, second-hand smoke exposure, alcohol consumption, and exercise habits). The current study involved 1,141 subjects and was conducted following relevant guidelines and legislation. Ethical approval was obtained from the Institutional Review Board of Chung Shan Medical University (CS1-20009).

Methylation array

The DNA methylation experiments were commissioned by the Taiwan Biobank and conducted by Health GeneTech Corp. Methylation levels were measured from blood samples using the Illumina Infinium MethylationEPIC BeadChip, which can detect over 850,000 CpG sites and quantified using β values (ranging from 0 to 1). Higher beta (β) values indicate higher methylation levels while lower values indicate lower methylation levels. In this study, the methylation level of the KLF14 promoter was determined by averaging the methylation levels across ten CpG sites on the KLF14 promoter: cg09823095, cg25109431, cg21449170, cg05651960, cg18751682, cg08097417, cg07955995, cg22285878, cg21520933, and cg06533629. These sites were the only ones identified within the CpG islands of the KLF14 promoter after extensive screening. Moreover, they have been linked to functional changes in gene expression through various mechanisms, including epigenetic modifications, transcription factor regulation, and evolutionary dynamics^1,14–16. We conducted additional analyses on individual CpGs to identify “key CpGs” that may drive most of the effect.

Covariate assessment

Participants were divided into four age groups (30–40, 40–50, 50–60, and 60–70) and included as covariates in the statistical analysis. Additional covariates accounted for in the models included smoking status alcohol use (never, former, and current), exercise habits (yes or no), (never, former, and current), exposure to second-hand smoke (yes or no), body mass index (BMI) classified by Taiwan’s Ministry of Health and Welfare (normal, underweight, overweight, and obese), waist-hip ratio thresholds (0.9 for men and 0.85 for women), and the presence of metabolic syndrome (yes or no). Information on these variables has been detailed in earlier publications^17,18.

Statistical analysis

We utilized the SAS software version 9.4 (SAS Institute, Cary, NC, USA) for comprehensive data management and statistical analysis. Our investigation focused on the relationship between sex and age with KLF14 promoter methylation, including the interaction between these factors, analyzed through multiple linear regression. To assess demographic differences by sex for categorical variables, we employed the chi-square (χ²) test, with results presented as mean ± standard error. A P value of less than 0.05 was considered statistically significant.

Furthermore, we addressed cell-type heterogeneity by implementing the reference-free adjustment method for cell-type composition (ReFACTor), as detailed in a prior study¹⁹. This approach enhanced the robustness of our analysis by minimizing type I errors and improving the statistical power of our findings.

In this study, methylation data for 10 CpG sites within the KLF14 promoter were available for a total of 1,142 individuals from the biobank. However, one individual with incomplete data was excluded from the analysis, resulting in a final sample size of 1,141 participants. Methylation levels were quantified using β values (ranging from 0 to 1), calculated with the formula M/(M + U), where M and U represent the intensities of methylated and unmethylated signals, respectively. We used beta values to determine methylation levels due to their intuitive interpretation, compatibility with high-throughput technologies (such as the Illumina Infinium BeadChip arrays), and the development of statistical models that enhance their analytical rigor.

Results

We observed notable differences in KLF14 promoter methylation between men and women (Table 1). Women exhibited a lower level of methylation (β = 0.0790, SE = 0.0005) compared to men (0.0812 ± 0.0005), and this difference was statistically significant (P = 0.0020). Men had a higher representation in the older age brackets. Regarding metabolic syndrome, there was no significant difference between men and women. Men had a prevalence of 18.52%, while women reported a slightly lower rate of 15.59%, showing that both groups exhibited similar susceptibility to this condition.

Table 1.

Demographic characteristics of the participants.

Variables	Women	Men	P value
	(n = 558)	(n = 583)
Mean KLF14 methylation (β ± SE)	0.0790 ± 0.0005	0.0812 ± 0.0005	0.0020
Age (years)			0.0071
30 ≤ Age ≤ 40	133 (23.84)	155 (26.59)
40 < Age ≤ 50	163 (29.21)	123 (21.10)
50 < Age ≤ 60	166 (29.75)	175 (30.02)
60 < Age ≤ 70	96 (17.20)	130 (22.30)
Cigarette smoking			< 0.0001
Never	521 (93.37)	332 (56.95)
Former	22 (3.94)	143 (24.53)
Current	15 (2.69)	108 (18.52)
Secondhand smoking exposure			0.2599
No	500 (89.61)	510 (87.48)
Yes	58 (10.39)	73 (12.52)
Alcohol consumption			< 0.0001
Never	544 (97.49)	481 (82.50)
Former	6 (1.08)	33 (5.66)
Current	8 (1.43)	69 (11.84)
Exercise			0.4044
No	319 (57.17)	319 (54.72)
Yes	239 (42.83)	264 (45.28)
BMI (kg/m2)			< 0.0001
Normal (18.5 ≤ BMI < 24)	315 (56.45)	225 (38.59)
Underweight (BMI < 18.5)	28 (5.02)	8 (1.37)
Overweight (24 ≤ BMI < 27)	123 (22.04)	209 (35.85)
Obesity (BMI ≥ 27)	92 (16.49)	141 (24.19)
Waist-hip ratio			0.6921
Men ≤ 0.9; women ≤ 0.85 (normal)	329 (58.96)	337 (57.80)
Men > 0.9; women > 0.85 (abnormal)	229 (41.04)	246 (42.20)
Metabolic syndrome			0.1882
No	471 (84.41)	475 (81.48)
Yes	87 (15.59)	108 (18.52)

Abbreviation: β = beta value; SE = standard error; BMI = body mass index.

Table 2 illustrates the relationship between age and sex in relation to KLF14 promoter methylation. Men exhibited higher KLF14 methylation levels compared to women, with a β coefficient of 0.00205, 95% CI = 0.00066–0.00345, and a p-value of 0.0039. Age was positively correlated with KLF14 methylation levels. Individuals in the 40 < Age ≤ 50 years age group showed a β coefficient of 0.00354, those in the 50 < Age ≤ 60 years group had a β of 0.00908, and those in the 60 < Age ≤ 70 years age group had the highest β coefficient of 0.01444 (P < 0.0001). This trend suggests that KLF14 methylation increases significantly with age. An interaction was also noted between sex and age (P = 0.0132).

Table 2.

Generalized model showing the association of KLF14 promoter methylation with sex, age, and study variables.

Variables	β	95% CI		P-value
Sex
Women (ref)	-	-	-	-
Men	0.00205	0.00066	0.00345	0.0039
Age
30 ≤ Age ≤ 40 (ref)	-	-	-	-
40 < Age ≤ 50	0.00354	0.00178	0.00530	< 0.0001
50 < Age ≤ 60	0.00908	0.00730	0.01087	< 0.0001
60 < Age ≤ 70	0.01444	0.01239	0.01650	< 0.0001
Cigarette smoking
Never (ref)	-	-	-	-
Former	−0.00014	−0.00205	0.00176	0.8833
Current	−0.00116	−0.00335	0.00102	0.2974
Secondhand smoking exposure
No (ref)	-	-	-	-
Yes	−0.00231	−0.00429	−0.00033	0.0222
Alcohol consumption
Never (ref)	-	-	-	-
Former	−0.00096	−0.00443	0.00250	0.5866
Current	−0.00043	−0.00299	0.00213	0.7418
Exercise
No (ref)	-	-	-	-
Yes	0.00005	−0.00127	0.00137	0.9384
BMI
Normal (ref)	-	-	-	-
Underweight	0.00191	−0.00169	0.00550	0.2983
Overweight	0.00008	−0.00143	0.00159	0.9190
Obesity	0.00027	−0.00159	0.00214	0.7727
Waist-hip ratio
Normal (ref)	-	-	-	-
Obesity	0.00074	−0.00068	0.00215	0.3064
Metabolic syndrome
No(ref)	-	-	-	-
Yes	0.00077	−0.00104	0.00258	0.4033

Abbreviation: β = beta value; BMI = body mass index, CI = 95% confidence interval.

In the regression analysis of KLF14 promoter methylation across age groups (Table 3), no significant differences were found between men and women in the 30 ≤ Age ≤ 40 (β = −0.00057, P = 0.6463) and 40 < Age ≤ 50 (β = 0.00088, P = 0.5137) age groups. However, in the 50 < Age ≤ 60 age group, men showed significantly higher methylation levels than women (β = 0.00336, P = 0.0108), and this difference continued in the 60 < Age ≤ 70 age group (β = 0.00472, P = 0.0238). Significant sex differences in KLF14 methylation were primarily observed in the older age groups. Further analysis (Table 4) indicated a similar pattern where KLF14 promoter methylation levels significantly increased with age in both sexes, with the most pronounced changes observed in the older age categories. These results were confirmed in the adjusted model examining the relationship between KLF14 promoter methylation levels and the combined effects of age and sex (Table 5). Notably, while women showed significant increases across all age categories, men only demonstrated significant increases starting from the 40 < Age ≤ 50 age group onward.

Table 3.

Regression analysis showing the association between KLF14 promoter methylation across age groups.

Variables	30 ≤ Age ≤ 40		40 < Age ≤ 50		50 < Age ≤ 60		60 < Age ≤ 70
	β	P-value	β	P-value	β	P-value	β	P-value
Sex
Women (ref)	-	-	-	-	-	-	-	-
Men	−0.00057	0.6463	0.00088	0.5137	0.00336	0.0108	0.00472	0.0238
Cigarette smoking
Never (ref)	-	-	-	-	-	-	-	-
Former	0.00080	0.6896	−0.00177	0.3196	−0.00115	0.5191	0.00119	0.6267
Current	0.00378	0.0357	−0.00144	0.4913	−0.00438	0.0406	−0.00530	0.1163
Secondhand smoking exposure
No (ref)	-	-	-	-	-	-	-	-
Yes	−0.00173	0.2590	−0.00368	0.0377	−0.00255	0.1905	−0.00125	0.7673
Alcohol consumption
Never (ref)	-	-	-	-	-	-	-	-
Former	−0.00821	0.0623	−0.00084	0.8241	0.00064	0.8415	−0.00199	0.5830
Current	−0.00358	0.1024	0.00002	0.9932	0.00392	0.1053	−0.00340	0.3355
Exercise
No (ref)	-	-	-	-	-	-	-	-
Yes	−0.00032	0.8069	0.00025	0.8401	−0.00028	0.8131	0.00013	0.9460
BMI
Normal (ref)	-	-	-	-	-	-	-	-
Underweight	0.00275	0.2685	−0.00038	0.8979	0.00261	0.5861	−0.00310	0.6786
Overweight	−0.00036	0.8089	−0.00009	0.9495	0.00254	0.0594	−0.00209	0.3036
Obesity	0.00148	0.3434	−0.00087	0.6210	−0.00085	0.6099	0.00303	0.2148
Waist-hip ratio
Normal (ref)	-	-	-	-	-	-	-	-
Obesity	0.00106	0.4515	0.00328	0.0155	0.00051	0.6798	0.00180	0.4294

Abbreviation: β = beta value; BMI = body mass index, CI = 95% confidence interval.

Table 4.

Linear regression illustrating the association between KLF14 promoter methylation and age categories by sex.

Variables	Women				Men
	β	95% CI		P-value	β	95% CI		P-value
Age
30 ≤ Age ≤ 40 (ref)	-	-	-	-	-	-	-	-
40 < Age ≤ 50	0.00294	0.00057	0.00530	0.0150	0.00358	0.00092	0.00623	0.0084
50 < Age ≤ 60	0.00723	0.00468	0.00979	< 0.0001	0.01037	0.00782	0.01293	< 0.0001
60 < Age ≤ 70	0.01241	0.00938	0.01544	< 0.0001	0.01554	0.01264	0.01844	< 0.0001

Abbreviation: β = beta value; CI = 95% confidence interval.

Adjusted for cigarette smoking, secondhand smoking exposure, alcohol drinking, exercise, BMI, waist-hip ratio, and metabolic syndrome.

Table 5.

Multiple linear regression analysis illustrating the relationship between KLF14 promoter methylation levels and the combined effects of age and sex.

Variables	β	95% CI		P-value
Women, 30 ≤ Age ≤ 40 (ref)	-	-	-	-
Women, 40 < Age ≤ 50	0.00307	0.00065	0.00549	0.0130
Women, 50 < Age ≤ 60	0.00715	0.00463	0.00967	< 0.0001
Women, 60 < Age ≤ 70	0.01223	0.00929	0.01517	< 0.0001
Men, 30 ≤ Age ≤ 40	0.00011	−0.00241	0.00262	0.9332
Men, 40 < Age ≤ 50	0.00376	0.00104	0.00648	0.0067
Men, 50 < Age ≤ 60	0.01082	0.00827	0.01338	< 0.0001
Men, 60 < Age ≤ 70	0.01624	0.01345	0.01903	< 0.0001

Abbreviation: β = beta value; BMI = body mass index, CI = 95% confidence interval.

Adjusted for cigarette smoking, secondhand smoking exposure, alcohol drinking, exercise, BMI, waist-hip ratio, and metabolic syndrome.

Discussion

Our findings reveal significant sex- and age-specific differences in KLF14 promoter methylation, contributing to a better understanding of the health disparities observed in metabolic syndrome and related conditions. Higher methylation levels in men compared to women, particularly in older age groups, suggest a potential biological mechanism underlying sex disparities in health outcomes. KLF14 plays a crucial role in metabolic regulation, particularly in adipose tissue, where it influences lipid metabolism and insulin sensitivity^20,21. The increased methylation of the KLF14 promoter in men may lead to altered gene expression, potentially exacerbating the risk of metabolic syndrome and related diseases in this population.

As previously mentioned, KLF14, a transcription factor involved in metabolic processes such as adipocyte differentiation and insulin sensitivity, plays a pivotal role in maintaining metabolic homeostasis. The observed increase in KLF14 promoter methylation in older men could contribute to alterations in metabolic pathways, leading to a higher risk of metabolic disorders such as obesity and type 2 diabetes. Hypermethylation of the KLF14 promoter may inhibit its expression, thus disrupting normal metabolic function and exacerbating age-related health issues. This mechanism could explain the heightened prevalence of metabolic syndrome found in older men in our study.

Conversely, the lower methylation levels in women, particularly those under 50, may reflect a protective metabolic profile. Women generally experience a more favorable lipid metabolism and insulin sensitivity compared to men, particularly pre-menopausally²². This is influenced by hormonal differences²³, body composition, and metabolic responses. However, these advantages can be influenced by age, specific health conditions, and hormonal changes²⁴. The influence of estrogen, known to modulate gene expression through epigenetic mechanisms, could promote KLF14 expression^25,26. This suggests that women may benefit from more robust KLF14 activity, potentially mitigating the risk of metabolic diseases.

The relationship between KLF14 methylation and age is particularly noteworthy, as our study indicates a positive correlation between age and methylation levels. This aligns with previous research associating aging with epigenetic changes that impact metabolic health²⁷. The findings that individuals aged 60–69 exhibit the highest levels of KLF14 methylation imply that age-related epigenetic modifications contribute to the increased prevalence of metabolic syndrome in older adults, who are more likely to experience dysregulation in metabolic processes²⁸.

The dysregulation of the KLF14 promoter with age can be contextualized within the broader framework of age-related epigenetic changes that affect gene expression and contribute to various pathologies. Significant epigenetic modifications, particularly DNA methylation occur in KLF14 as individuals age. This phenomenon is not unique to KLF14; rather, it reflects a general trend observed across numerous genes, where age-associated changes in DNA methylation patterns lead to altered gene expression profiles. The epigenetic dysregulation of KLF14, especially its promoter hypermethylation, has been attributed to several mechanisms, including stochasticity, environmental exposures, and inherent sex differences in metabolism^9,29. Random error in biological processes plays a significant role in epigenetic modifications. The aging process, for instance, is associated with increased DNA methylation variability, leading to hypermethylation of specific genes such as KLF14. Studies have shown that the methylation status of KLF14 is linked to aging in various human tissues, including blood and buccal cells, suggesting that stochastic epigenetic drift may contribute to its dysregulation over time^2,30.

Environmental exposures also significantly influence DNA methylation patterns. Factors such as diet, pollution, and lifestyle choices can alter the epigenetic landscape. For instance, high progesterone levels during in vitro fertilization cycles have been linked to changes in DNA methylation of various genes, indicating that hormonal environments can modulate epigenetic regulation³¹. Additionally, the interaction of environmental factors with genetic predispositions may exacerbate the hypermethylation of KLF14 in men, who may have different exposure profiles compared to women⁹.

Inherent sex differences in metabolism further complicate the landscape of KLF14 dysregulation. Research indicates that KLF14 is involved in lipid metabolism and insulin signaling, with its effects being more pronounced in women than in men¹². This sex-specific regulation suggests that metabolic pathways influenced by KLF14 may be subject to different epigenetic controls, potentially leading to greater promoter hypermethylation in men. Moreover, studies have shown that genetic variants associated with KLF14 are linked to metabolic traits, and these associations tend to differ by sex, highlighting the role of sex-specific metabolic differences in KLF14 regulation^9,12.

Interestingly, our study did not reveal significant differences in metabolic syndrome prevalence between sexes, which contrasts with the established understanding that men generally exhibit a higher risk for metabolic syndrome. This discrepancy may be attributed to the role of KLF14 methylation in metabolic pathways, suggesting that while methylation levels differ by sex, the resultant health outcomes may not be as straightforward. The interaction between sex and age in relation to KLF14 methylation highlights the complexity of the disease etiology.

Additionally, we did not observe significant differences in KLF14 promoter methylation between individuals with and without metabolic syndrome. This raises questions about the role of KLF14 as an independent risk factor and its potential interactions with environmental factors such as smoking and diet. However, it is essential to consider that KLF14 has been implicated in various metabolic processes, suggesting that its role may not be solely independent but rather part of a complex network influenced by environmental factors. For instance, KLF14 is known to regulate lipid metabolism and adiposity, with studies indicating that its expression in adipocytes can lead to favorable metabolic outcomes, particularly in response to high-fat diets²⁵. This suggests that while KLF14 may not show significant methylation differences in MetS, its functional role in metabolic regulation could still be significant, particularly under varying environmental exposures.

Research indicates that methylation status is a critical regulator of KLF14 activity. KLF14 plays a significant role in various cellular processes, including inflammation and metabolism, and its expression is modulated by epigenetic mechanisms, particularly DNA methylation. Hyper-methylation of the KLF14 promoter is associated with decreased expression of this gene. For instance, Wężyk et al. demonstrated that hypermethylation of KLF14 influences cell death signaling pathways, suggesting a functional link between methylation status and gene expression in the context of neurodegenerative diseases like Alzheimer’s³². Similarly, Hou et al. highlighted that downregulation of KLF14 correlates with increased cellular senescence, further supporting the notion that methylation can negatively impact KLF14 expression and its associated cellular functions². Additionally, Horvath et al. noted that methylation of the KLF14 locus in various tissues, including adipose tissue, is predictive of chronic inflammation, further underscoring the role of methylation in regulating KLF14 expression and its downstream effects on health³³.

Furthermore, the interaction between age and sex in relation to KLF14 methylation emphasizes the necessity for personalized approaches to health management. Strategies aimed at reducing the risk of metabolic disorders in older men may need to focus on lifestyle modifications that can influence methylation patterns, such as increased physical activity, dietary changes, and possibly hormonal interventions. The study’s findings resonate with the broader literature on epigenetic modifications and their implications for metabolic health. Alterations in DNA methylation patterns have been linked to various metabolic disorders, including insulin resistance and obesity^34,35.

In light of the potential implications of KLF14 methylation on metabolic health, exploring ways to intervene and modify methylation patterns becomes crucial. Several strategies have shown promise in influencing DNA methylation. Evidence suggests that lifestyle modifications may influence DNA methylation positively, thus promoting healthier outcomes. For instance, adopting a balanced diet rich in fruits, vegetables, and whole grains has been shown to alter DNA methylation patterns favorably. Studies indicate that adherence to a Mediterranean diet can be associated with decreased DNA methylation in obesity-related genes and improved metabolic health indicators^36–38.

Regular physical activity further complements dietary interventions, as exercise has been documented to induce beneficial changes in DNA methylation profiles. Studies indicate that engagement in consistent exercise routines can lead to significant shifts in DNA methylation patterns related to metabolic pathways, thereby enhancing overall metabolic health^38,39. The intricate interplay between diet, exercise, and DNA methylation underscores the importance of lifestyle as a modifiable factor in mitigating risks associated with metabolic disorders.

Pharmacological interventions also present compelling therapeutic avenues for influencing gene expression through epigenetic mechanisms. Agents such as histone deacetylase inhibitors and DNA methyltransferase inhibitors are being actively researched for their roles in altering gene expression patterns associated with metabolic disease and obesity⁴⁰. Hormonal therapies may also affect KLF14 expression and its methylation status, particularly in post-menopausal women, where fluctuations in estrogen levels can influence gene regulation^41,42. Moreover, nutraceuticals, including compounds like folate and vitamin B12, serve as essential methyl donors that may play a crucial role in shaping DNA methylation patterns, further emphasizing the potential for targeted nutritional strategies to improve metabolic health^42,43.

These potential interventions highlight the importance of personalized approaches to health management, especially for older men at higher risk for metabolic disorders. Strategies aimed at reducing the risk of metabolic syndrome could heavily incorporate lifestyle changes that influence methylation patterns, such as increased physical activity and dietary modifications tailored to individual metabolic profiles⁴⁴. The convergence of lifestyle, pharmacological, and nutraceutical approaches may offer a comprehensive strategy to manage and potentially reverse the changes in DNA methylation that are associated with metabolic diseases, thus enhancing metabolic health outcomes.

While this study provides valuable insights, several limitations should be acknowledged. First, assessing average methylation levels across 10 CpG sites may oversimplify the complex regulatory landscape of the KLF14 promoter. This approach does not account for potential heterogeneity between individual CpG sites, which could influence gene expression differently. However, we conducted additional analyses on individual CpGs alongside the mean beta values, finding that only cg08097417 and cg07955995 were statistically significant, as detailed in Supplementary Table 1. Despite this, future research should consider analyzing each CpG site independently to elucidate site-specific effects on KLF14 function and metabolic outcomes. Additionally, the Taiwan Biobank database lacks information on important factors such as medication use and pre-existing health conditions, which could significantly influence DNA methylation patterns and metabolic health. The absence of these data limits the generalizability of our findings and the ability to draw definitive causal relationships between KLF14 methylation and health outcomes. Lastly, the cross-sectional nature of this study restricts our ability to infer temporal relationships between age, sex, and KLF14 methylation. Longitudinal studies are necessary to track how methylation patterns evolve and their implications for metabolic health across different demographic groups.

Conclusion

Our study highlights the critical role of KLF14 promoter methylation as a mediator of sex- and age-related disparities in metabolic health. These findings underscore the need for further research, as understanding these epigenetic modifications enhances our knowledge of the biological causes of metabolic disorders. Ultimately, this insight could guide the development of targeted interventions for metabolic syndrome and related conditions.

Electronic supplementary material

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Abbreviations

KLF14

Krüppel-like factor 14

TWB

Taiwan Biobank

Author contributions

Conceptualization: C.H.C. and Y.P.L.; Data curation: W.Y.L.; Formal analysis: W.Y.L.; Methodology: O.N.N. and Y.P.L.; Supervision: Y.P.L.; Writing — original draft: C.H.C.; Writing — review & editing: C.H.C., Y.C. C. and O.N.N. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the National Science and Technology Council (NSTC 113-2121-M-040-001, NSTC 112-2121-M-040-002, NSTC 113-2811-M-040-001, NSTC 112-2811-M-040-001), Taiwan.

Data availability

The data that support the findings of this study are available from Taiwan Biobank but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however available from the corresponding author, Prof. Yung-Po Liaw upon reasonable request and with permission of Taiwan Biobank.

Ethics declarations 

Competing interests

The authors declare no competing interests.

Consent for publication

All co-authors have agreed to the submission and publication of this manuscript.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Chung Shan Medical University (CS1-20009). The study was conducted in accordance with relevant guidelines and legislation.