The Common FTO rs9939609 Polymorphism Interacts with Sleeping and Eating Windows to Affect Predisposition to Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a multifactorial metabolic disease caused by malfunction of the insulin action pathway [1]. T2DM is influenced by a complex interaction between genetic predisposition and environmental factors such as physical activity (PA) and diet [2,3]. The heritability of T2DM is considerable and estimated to range between 31 and 72% [4]. Although obesity remains one of the strongest risk factors for T2DM, up to 30% of the risk is attributable to other modifiable lifestyle factors, either directly or through their contribution to adiposity [5].

Genetic variants within the fat mass and obesity-associated gene (FTO) have been consistently linked to diabetes risk, independent of body mass index (BMI) [6,7,8]. A genome-wide association study (GWAS) confirms that rs9939609 variant located within the first intron of the FTO is associated with increased adiposity, insulin resistance, and impaired glucose homeostasis [9,10,11]. Although rs9939609 is an intronic variant, growing evidence demonstrates that this region functions as a regulatory element. Obesity-associated SNPs within intron 1 of FTO reside in enhancer-like regions that influence the expression of multiple genes involved in energy homeostasis and adipocyte biology [12,13,14]. Beyond transcriptional regulation, FTO functions as an N6-methyladenosine (m6A) RNA demethylase, thereby modulating post-transcriptional control of genes involved in adipogenesis, lipid metabolism, inflammation, and vascular dysfunction, all of which are relevant to the pathogenesis of obesity and T2DM [15,16].

Functional studies have shown that FTO expression in metabolic tissues such as skeletal muscle and pancreatic islets may influence insulin signaling, lipogenesis, and mitochondrial activity, suggesting a direct contribution to glucose dysregulation beyond its role in obesity [17,18,19]. Additionally, the FTO locus regulatory activity affects the expression of genes such as IRX3 and IRX5, which play key roles in adipocyte differentiation, thermogenesis, and metabolic regulation [12,13]. Accumulating evidence indicates that lifestyle behaviors, particularly those related to circadian alignment, can modify and interact with the metabolic effects of FTO variants [20,21,22,23,24,25,26]. FTO has been shown to modulate circadian biology by inhibiting CLOCK–BMAL1 induced transcription and interacting with cryptochromes, thereby linking FTO activity to the core molecular clock machinery [27]. Furthermore, evidence accumulated over the last decade indicates that the pace of the circadian clock is controlled by m6A-dependent RNA processing. For instance, suppression of m6A methylation and its impact on RNA processing are sufficient to slow down the circadian clock [28,29]. Notably, FTO is highly expressed in the hypothalamus, where it regulates energy intake and feeding behavior. Carriers of the rs9939609 A allele exhibit dysregulated ghrelin signaling and impaired postprandial appetite suppression. Furthermore, the most prevalent SNP rs9939609 risk allele was found to be associated with increased FTO mRNA levels in adipose tissue and peripheral blood mononuclear cells and skin biopsies [30,31,32].

Emerging lifestyle factors related to circadian alignment have gained increasing attention as potential modulators of glucose metabolism and cardiometabolic health [1,3,26,33]. Given the involvement of FTO in circadian regulation and appetite control, behavioral timing may represent interface through which genetic susceptibility translates into metabolic risk. Disrupted sleep or irregular eating patterns have been shown to impair insulin sensitivity, increase glucose levels, and disturb hormonal rhythms regulating metabolism [5,34,35]. Despite these advances, limited evidence exists regarding direct gene–environment factors such as eating and sleeping windows interactions in general and between FTO rs9939609 and behavioral factors in relation to T2DM in particular [2,3]. Previous studies have primarily examined the main effects of FTO or lifestyle factors separately, with few exploring their combined influence on metabolic outcomes [17,20,21,22].

The timing and regularity of sleep and eating behaviors are increasingly recognized as critical determinants of glycemic control and T2DM risk. The American Heart Association highlights that greater rhythmicity in lifestyle behaviors, including consistent sleep and meal timing, is associated with lower risk of glycemic dysregulation and T2DM, while irregular patterns are linked to increased risk, independent of traditional risk factors [36]. The American Diabetes Association emphasizes that both short (<6 h) and long (>9 h) sleep durations, poor sleep quality, and evening chronotype confer a significantly higher risk of T2DM [37]. Recent evidence suggests that the adverse metabolic effects of the FTO risk allele may be amplified by excessive fuel availability [38]. Circadian misalignment occurs when the body’s internal clock is out of sync with external environmental cues (like light–dark cycles, sleeping or eating patterns), which leads to hormonal imbalances, impaired glucose metabolism, and gut microbiome dysbiosis [39], resulting in excessive energy storage and increased risk of metabolic disorders such as obesity and T2DM.

We aimed to study the association between FTO rs9939609, modifiable lifestyle behaviors such as eating and sleeping behaviors, and T2DM risk in a large Israeli adult cohort. We hypothesized that specific lifestyle behaviors, particularly those related to circadian and behavioral timing, may modify the genetic susceptibility to T2DM.

This study examined the relationship between FTO rs9939609, lifestyle factors, and T2DM risk in a large Israeli cohort of 12,254 adults. Carriers of the FTO A risk allele exhibited higher odds of T2DM, supporting the established role of FTO in metabolic regulation [8], across diverse populations [8,43], including the Israeli and Middle Eastern cohorts [44,45]. The elevated risk for T2DM in FTO risk carriers is partly mediated by the increased adiposity. However, meta-analyses and large cohort studies indicate an independent effect of FTO on T2DM susceptibility [8,43,46], likely through direct effects on glucose metabolism and insulin sensitivity [43]. For instance, FTO expression in human islet cells is not associated with BMI, and increased FTO levels in muscle have been linked to altered insulin signaling, enhanced lipogenesis, oxidative stress, and mitochondrial dysfunction during T2DM [18].

Previous studies have shown that time-restricted eating window (e.g., 8–10 h) and longer fasting window can improve insulin sensitivity and glycemic control in adults with T2DM [3,8,47]. However, to date, for the best of our knowledge, genetic predisposition in this context was not studied. Our novel findings demonstrate significant association between prolonged eating windows and shorter fasting windows with higher T2DM risk among FTO rs9939609 A allele carriers.

Adjustment for BMI attenuated the association between FTO rs9939609 and T2DM, consistent with adiposity acting as a partial mediator of the genetic effect. In contrast, the gene–lifestyle interactions of FTO rs9939609 with eating window and sleep related variables remained largely unchanged after BMI adjustment. This pattern suggests that while adiposity contributes to overall T2DM risk, the observed gene–behavior interactions are not solely explained by BMI and may reflect BMI-independent pathways related to behavioral timing or circadian regulation. Beyond the interaction detected in the regression models, exploratory decision tree analyses suggested that the predisposition of FTO rs9939609 may be concentrated within specific behavioral ranges. For eating window duration, the tree identified the following categories: ≤8 h, 8–14 h, and >14 h. Those intervals correspond to the median daily eating window in United States adults of 14 h [48], and to the eating window of 8 h which is linked to metabolic benefits including insulin resistance improvements [46]. Importantly, the gene–environment interaction emerged specifically within the mid-range of 8–14 h, where FTO A allele carriers showed higher T2DM prevalence compared to TT individuals. In contrast, at eating windows >14 h and ≤8 h, T2DM prevalence was high and low, respectively, for overall population. These findings imply that FTO-related susceptibility is amplified when eating window duration corresponds to specific time window (8–14 h); above this time window the risk for T2DM is high for the overall population (regardless of FTO susceptibility).

In relation to T2DM, under circadian misaligned conditions, such as those induced by irregular sleep and eating patterns, lipid metabolism in skeletal muscle may be disrupted, including phosphatidylcholine species [49,50]. Carriers of the FTO rs9939609 risk allele exhibit genotype-dependent changes in serum phosphatidylcholine metabolites, which are established metabolic markers of obesity and T2DM [51]. It is therefore plausible that the coexistence of FTO risk allele carriage and circadian disruption contributes to alterations in skeletal muscle lipid composition, potentially reducing insulin sensitivity and impairing glucose homeostasis.

Timing of the last meal appeared to modulate T2DM risk through its interaction with FTO rs9939609, supporting evidence that late-night eating can impair metabolic regulation through circadian disruption. Aligned with our results, the American Heart Association states that eating the last meal after 9:00 pm, compared to before 8:00 pm, is associated with a 28% higher risk of T2DM [36]. Nonetheless, recent interventional studies and randomized controlled trials have not demonstrated that modifying meal timing in individuals who are carriers of the FTO risk allele leads to measurable improvements in glycemic control or T2DM incidence [26,52]. Both FTO risk allele carriers and non-carriers experienced similar improvements in fasting blood glucose, insulin levels, and insulin resistance after dietary interventions, with no statistically significant gene–diet interaction for glycemic outcomes [52].

Sleep duration and bedtime onset emerged in a significant association with FTO rs9939609 and T2DM risk. Longer night sleep and later bedtime onset were associated with increased T2DM risk among A risk allele carriers. While insufficient sleep is a well-established metabolic risk factor, the current findings suggest that within the recommendation [37], prolonged sleeping hours [27,30] may reflect metabolic vulnerability associated with FTO risk alleles, while above this time window the risk for T2DM is high for the overall population. Tree analysis clarified that the genotype effect appeared specifically within 6–8 h, where FTO A allele carriers had higher T2DM prevalence. In contrast, very short sleep (<5 h) and prolonged sleep (≥9 h) displayed elevated diabetes prevalence overall, but without genotype-based splits suggesting that at exposure extremes the behavioral factor predominates the FTO genetic predisposition. Our findings align in part with Li et al. (2021), who observed that both short and long sleep durations, as well as late chronotype, were associated with increased risk of T2DM in a large prospective UK cohort [34]. They showed that these associations persisted across different levels of polygenetic risk, suggesting that sleep and circadian behaviors independently contribute to diabetes susceptibility [34,53,54].

Subjective poorer sleep quality was associated with higher T2DM risk, and genetic predisposition interaction was observed in the logistic regression model. In the decision tree analyses, poorer sleeping quality significantly elevated T2DM risk regardless of FTO genetic background, while fair or good sleep quality interacted with FTO predisposition, where carriers of FTO risk allele exhibited nearly double the T2DM prevalence. This pattern, together with the observation of increased risk within the recommended range of sleep duration, may be consistent with reports linking FTO risk alleles to obstructive sleep apnea [55]. Furthermore, subjective sleep quality ratings do not necessarily reflect the frequency of short awakenings or objective sleep depth [56], and thus individuals with different genotypes may experience or interpret “good” or “fair” sleep differently.

Our findings should be interpreted cautiously as the cross-sectional nature of the study precludes causal inference. Reverse causality is plausible, particularly given that metabolic dysregulation and T2DM diagnosis may lead to behavioral changes in eating patterns and sleep habits; it is also plausible that carriers of the FTO rs9939609 risk allele are inherently more prone to circadian dysregulation. Evidence suggests that FTO is involved in central clock regulation, appetite signaling, and behavioral timing [27,32], which may predispose genetically susceptible individuals to irregular eating and sleep patterns. Moreover, although infancy-related exposures were not examined in the present study, the postnatal period represents a vulnerable window for circadian clock programming, during which feeding patterns act as chronobiological signals [57]. Disruptions during this early period, such as formula feeding compared with breastfeeding, may contribute to long-term circadian and metabolic vulnerability [58]. Barragán et al. emphasized the clinical relevance of meal and sleep timing in the context of genetically driven obesity risk in Mediterranean population [59]. Specifically, Barragán et al. found significant gene–sleep interactions between the midpoint of sleep and FTO-rs9939609 polymorphism which played a role in determining obesity phenotypes [59]. Our results extend these observations to T2DM, indicating that circadian-related eating and sleeping patterns may represent clinically relevant factors for personalized metabolic risk stratification among FTO rs9939609 risk allele carriers. Although several gene–lifestyle interactions reached statistical significance, the observed effect sizes were modest, which is expected given the multifactorial nature of circadian regulation and behavioral timing. Such effects are therefore likely to be most relevant at the population level and over longer time horizons, rather than as deterministic predictors at the individual level. From a clinical perspective, these findings support assessing behavioral timing and circadian-related lifestyle patterns as part of metabolic risk stratification, particularly among individuals with known genetic susceptibility.

The study’s strengths include its large sample size, detailed assessment of sleeping and eating behaviors, and the examination of multiple genetic models. However, several limitations should be noted: lifestyle behaviors were self-reported and therefore subject to recall and reporting bias; the cross-sectional design precludes causal inference; and most lifestyle variables were self-reported and therefore subject to recall and perception bias.

The common FTO rs9939609 variant is significantly associated with elevated T2DM risk. Longer eating window and shorter fasting window, the timing of the last meal, typical bedtime onset, and habitual sleep duration and quality may all play a role in shaping how genetic predisposition affects T2DM. Certain behavioral choices, such as eating windows within approximately 8–14 h, early last-meal timing, and bedtime earlier than 23:00, may represent intervals in which genotype related differences become more pronounced. Our results underscore potential avenues for future research exploring how circadian-behavior-linked factors may interact with genetic risk to influence metabolic health.

We acknowledge MyGenes for the data.

The following abbreviations are used in this manuscript: Type 2 diabetes mellitus (T2DM); Physical activity (PA); Fat mass and obesity-associated gene (FTO); Body mass index (BMI); Single nucleotide polymorphism (SNP); Genome-wide association studies (GWAS); Hardy–Weinberg equilibrium (HWE); Standard deviation (SD).

L.K.N., D.C., and R.B., conceptualization, methodology, software, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, visualization. R.B., resources, data curation, supervision. All authors have read and agreed to the published version of the manuscript.

The study was approved by the ethical committee of Ariel University (#AU-HEA-RB-20220214, 25 May 2023).

Informed consent was obtained from all subjects involved in the study.

No new data were created or analyzed in this study.

L.K.N. and D.C. declare no conflicts of interest. R.B. is a scientific consultant of MyGenes.

This research received no external funding.