This study provides novel evidence linking chrono-nutrition, dietary GI (as a mixture of biomolecules), and genetic variation in FAAH with fasting metabolic outcomes among adults with obesity. The main finding was that dinner GI showed a consistent inverse association with fasting glycemia, even after adjustment for age, sex, BMI, energy intake, FAAH risk allele, and dinner protein. In this context, the GI concept was designed to account for not only glucose intake but also the putative impact of associated macromolecules, micronutrients, and nutritional biofactors on glycemia [29]. Additionally, total daily GI was positively associated with fasting insulin concentrations and HOMA-IR levels while also showing an inverse relationship with fasting glycemia. Furthermore, the FAAH C385A variant was independently associated with fasting insulin and HOMA-IR levels. Together, these results suggest that both the quality and timing of carbohydrate intake, in combination with genetic predisposition, contribute to fasting metabolic profiles in individuals with obesity.

Previous large-scale evidence has consistently shown the detrimental metabolic impact of high-GI and high-GL diets. In three major United States cohorts encompassing more than 3.8 million person-years of follow-up and over 15,000 incident cases of patients with type 2 diabetes, Bhupathiraju et al. [6] reported that the participants in the highest GI quintile had a 33% greater risk of developing type 2 diabetes, while those in the highest GL quintile had a 10% higher risk, particularly when combined with low cereal fiber intake. Recent work incorporating chrono-nutrition suggests that carbohydrate timing intake modifies glycemic responses. Circadian regulation leads to greater insulin sensitivity and glucose tolerance in the morning, with a progressive physiological decline toward the evening, as highlighted by Henry et al. [30], supporting the concept that misaligned eating patterns may impair glycemic responses.

Evidence from time-restricted eating studies supports the importance of meal timing in metabolic regulation. In a randomized crossover trial, restricting the daily eating window to earlier hours improved glucose tolerance, fasting insulin levels, and insulin sensitivity compared with late eating, independent of energy intake. These findings suggest that aligning food intake with circadian rhythms may enhance metabolic control and reinforce the notion that eating later in the day can impair glycemic responses [31]. Similarly, in a crossover study, Morgan et al. [8] demonstrated that consuming high-GI meals late in the evening resulted in higher interstitial glucose levels and impaired postprandial insulin sensitivity compared to equivalent meals consumed earlier in the day. In a clinical weight loss trial in which dinner carbohydrate intake was increased, higher levels of glycemic biomarkers were observed; however, the magnitude of the increase differed when the high-CHO meal was consumed at lunch [32]. These findings support the concept that late high-GI meals exacerbate circadian misalignment of glucose metabolism, probably involving regulatory mechanisms like decreasing insulin sensitivity [33] associated with the concurrence of food intake and high levels of melatonin [34], and may contribute to metabolic dysregulation, which is also affected by genetic factors [35]. Also, chronotype is a biologically plausible modifier of the relationship between meal timing, carbohydrate quality, and metabolic regulation [36]. Future studies should explicitly address circadian misalignment between endogenous metabolic rhythms and behavioral patterns, such as meal timing and sleep–wake cycles.

Complementary evidence from bodyweight-loss maintenance studies also highlights the role of dietary GI. The DiOGenes multicenter trial showed that diets with modestly higher protein content or lower GI values improved long-term bodyweight maintenance following an initial weight reduction. Participants in the high-protein or low-GI groups regained less body weight compared with those adhering to low-protein or high-GI diets, demonstrating that modest dietary adjustments can sustain bodyweight loss and improve adherence [37]. Moreover, the timing of caloric distribution has been directly compared in intervention studies. In a 12-week randomized trial conducted on overweight and obese women with metabolic syndrome, Jakubowicz et al. [10] reported that a high-calorie breakfast led to greater reductions in bodyweight and waist circumference than an isocaloric diet involving high caloric intake at dinner. The breakfast-rich diet also yielded more pronounced improvements in fasting glucose, insulin, HOMA-IR, lipid profile, and postprandial responses, underscoring the metabolic advantages of concentrating caloric and carbohydrate intake earlier in the day.

In this study, the positive associations between dietary GI, insulin, and HOMA-IR levels observed are consistent with prior reports [38,39]. Liese et al. [39], in the Insulin Resistance Atherosclerosis Study, observed that neither GI nor GL remained independently associated with insulin sensitivity after multivariate adjustments. However, significant associations between GL and total carbohydrate intake were present before adjusting for energy intake, suggesting that the glycemic characteristics of the diet may contribute to early metabolic disturbances prior to the influence of overall caloric consumption. Similarly, O’Sullivan et al. [38] found that GL, particularly at lunch, independently predicted higher HOMA-IR levels in older women. These results support the notion that diets with a higher glycemic impact can contribute to early alterations in glucose–insulin homeostasis, a finding consistent with the pattern observed in our cohort.

Taken together, previous studies generally indicate that late high-GI meals impair glucose handling. In this context, the inverse association between dinner GI and fasting glucose observed in our study could still be compatible with compensatory increases in insulin secretion to preserve euglycemia in early stages of insulin resistance [40]. This stage is clinically relevant because it remains amenable to improvement through lifestyle and pharmacological interventions. For example, Azócar-Gallardo et al. [41] demonstrated that concurrent training, with or without metformin, improved HOMA-IR and fasting insulin levels in individuals with early insulin resistance.

At the molecular level, circadian regulation of glucose–insulin homeostasis is mediated not only by systemic hormonal rhythms but also by peripheral metabolic clocks [30]. Core clock proteins, particularly CLOCK and BMAL1, regulate the transcription of genes involved in β-cell function, hepatic glucose production, and insulin signaling. An experimental disruption of CLOCK or BMAL1 in animal models results in reduced insulin secretion, impaired glucose tolerance, and increased adiposity. SIRT1, a nutrient- and redox-sensitive deacetylase, directly modulates clock activity by promoting BMAL1–CLOCK transcriptional function and has been shown to link metabolic state with circadian gene expression [42]. Reduced SIRT1 activity or circadian misalignment leads to decreased insulin sensitivity and increased oxidative and metabolic stress [43]. In this context, late carbohydrate intake, occurring during a phase of reduced circadian metabolic efficiency, may influence fasting glucose regulation, especially in subjects with the rs3749474 variant of the CLOCK gene [44], a finding that is consistent with the inverse association observed in this study between dinner GI and fasting glycemia.

The endocannabinoid system is another important modulator of energy balance and glucose metabolism [45]. FAAH, the main enzyme responsible for degrading endocannabinoids such as anandamide (AEA), influences insulin sensitivity and lipid storage [46]. The FAAH C385A polymorphism (Pro129Thr) reduces FAAH activity and increases levels of circulating endocannabinoids, effects that have been associated with obesity, glucose–insulin homeostasis, and cardiometabolic risk [47,48]. Interestingly, in our study, the A allele was associated with lower fasting insulin and HOMA-IR levels after adjustment. This may reflect differences in genetic background, environmental exposure, disease stage, or dietary patterns. Recent reviews have highlighted that associations between the FAAH C385A variant and metabolic traits are inconsistent across studies and populations, underscoring the context-dependent expression of this polymorphism [49,50].

From a molecular perspective, the metabolic effects associated with the FAAH C385A variant can be interpreted through endocannabinoid signaling. Reduced FAAH activity results in higher circulating anandamide levels, which increases activation of the cannabinoid receptor CB1 to promote orexigenic drive, lipogenesis, and mitochondrial oxidative capacity [51]. In several reports, this phenotype correlates with increased adiposity and insulin dysregulation [49]. However, its metabolic impact is not uniform. An increasing amount of evidence is showing that obesity is not metabolically homogeneous. Individuals with comparable adiposity may differ markedly in terms of glucose tolerance, fasting insulin levels, systemic inflammation, and cardiometabolic risk, giving rise to metabolically healthy and unhealthy obesity phenotypes [52]. Such variability may result from differences in adipose tissue function, hormonal adaptation, and genetics [53]. Within this framework, the lower fasting insulin and HOMA-IR levels observed in A-allele carriers may reflect a compensated metabolic profile despite excess weight.

Other biological systems may contribute to the variability in insulin resistance. For instance, Aljuraiban et al. [54] showed that gut microbiome composition was associated with fasting insulin levels, HOMA-IR levels, and adiposity distribution in women with obesity, reinforcing the multifactorial nature of metabolic dysregulation.

The interaction between dietary factors and genetic variation is a central topic in nutrigenetics. No significant interaction was detected in this study; however, our data indicate a plausible dinner-specific GI–FAAH–glucose/insulin regulatory nexus. This chrono-nutrigenetic perspective suggests that dietary carbohydrate quality and timing may differentially affect individuals according to their genetic background. While most previous nutrigenetic studies focused on macronutrient proportions or total GI/GL [55], our data emphasize the importance of evaluating the timing of carbohydrate intake. Future intervention studies should test whether evening low-GI meals confer metabolic advantages in individuals carrying the FAAH A allele.

These findings support the need to move beyond total daily nutrient intake and incorporate both timing of intake and genetic susceptibility into dietary recommendations. Personalized nutrition approaches could benefit individuals with obesity by tailoring carbohydrate quality not only by quantity and type but also by the timing of consumption. In clinical practice, counseling patients with obesity to reduce dinner GI values may represent a simple strategy for improving fasting glucose–insulin balance, particularly among those genetically predisposed to issues. Researchers should study whether the time at which foods with a high GI value or meals with a high GL are consumed is also associated, in longitudinal studies, with a higher risk of developing type 2 diabetes [56] and whether carrying this variant could involve a greater disease burden.

This study has several notable strengths. A major strength is its comprehensive characterization of the participants (selected with clearly defined inclusion and exclusion criteria), integrating anthropometric, biochemical, dietary, and genetic data (variables obtained with standardized protocols and by trained personnel), enabling a multifactorial evaluation of chrononutrition, carbohydrate quality, and FAAH C385A genetic variability. Its precise estimation of GI at each eating occasion, including a specific calculation for dinner GI, represents an important methodological advantage over previous studies, which focused solely on total daily GI/GL. The use of multivariable models adjusted for relevant confounders, such as age, sex, BMI, energy intake, FAAH genotype, and dinner protein, further reinforces the internal validity of the findings. In any case, the role of dietary protein in our findings may require further attention. For example, dietary protein, in conjunction with glycemic index, may affect metabolic outcomes and should not be discarded in future studies.

Nevertheless, some limitations must be acknowledged. This study’s cross-sectional design precludes causal inference between dinner GI, FAAH genotype, and fasting glucose–insulin outcomes. This study was conducted exclusively among adults with obesity, a population characterized by altered glucose–insulin regulation. Therefore, these results cannot be generalized to non-obese or metabolically healthy populations. Future studies conducted across diverse metabolic phenotypes are needed. Dietary assessment based on self-reported 24 h records may introduce recall bias and poses a risk of underreporting, particularly for individuals with obesity. Only a single genetic variant within the endocannabinoid pathway was examined, without considering additional genes or molecular markers (e.g., inflammatory or oxidative stress parameters) that could have provided deeper mechanistic insights [17]. Moreover, although the sample size was sufficient to detect the main genetic and dietary associations, identification of subtle gene–diet interaction effects may require larger samples. Additionally, the lack of circadian phase markers and sleep parameters limited our ability to directly assess circadian misalignment or postprandial metabolic responses and therefore warrants a cautious interpretation of the chrononutrigenetic implications of FAAH. Despite these limitations, this study makes a novel contribution by integrating chrononutritional and nutrigenetic perspectives to help us to better understand metabolic regulation in adults with obesity, although the data were collected several years ago. However, the novelty of this work lies not in the timing of data collection but in the research question, conceptual framework, and interpretation, which are both innovative and timely. We consider reexamining well-characterized datasets through emerging scientific perspectives —such as chrononutrition and nutrigenetics—to be a valid and valuable approach that can make a meaningful contribution to the current literature.

Notably, the GI represents a combination of biomolecules that influence post-prandial glycemia in addition to glucose intake [4,57,58]. We connect our findings with these factors by examining how dietary patterns and genetic variants may interact with internal circadian regulation to shape metabolic risk profiles, particularly because the FAAH gene is involved in behavior and sex-specific neuroinflammatory alterations induced by life stress [17].