Factorii nutriţionali și ai stilului de viaţă asociaţi cu rezistenţa la insulină la adulţii tineri – rezultatele unui studiu transversal

Introduction

Insulin resistance is one of the most prevalent metabolic disorders of the 21^st century, defined as the diminished response of peripheral tissues, particularly skeletal muscle, liver and adipose tissue to insulin action, with the direct consequence of compensatory pancreatic hypersecretion and hyperinsulinemia⁽¹⁾. This metabolic dysfunction constitutes a central link in the progression toward type 2 diabetes mellitus, metabolic syndrome, atherogenic dyslipidemia and non-alcoholic fatty liver disease.

While insulin resistance was traditionally associated predominantly with older populations or individuals with obesity, recent epidemiological data significantly shift this perspective. Parcha et al. demonstrated that approximately 40% of American adults aged 18 to 44 years old exhibit insulin resistance, with half of them having a Body Mass Index (BMI) within the normal range⁽²⁾. These figures underscore that insulin resistance is a common subclinical condition among young adults, often unrecognized in the absence of active metabolic screening.

Modern lifestyle plays a determining role in the onset and progression of insulin resistance. Frequent consumption of high glycemic index carbohydrates generates repeated glycemic fluctuations, which overburden pancreatic insulin secretion and progressively reduce insulin receptor sensitivity⁽³⁾. Sedentary behavior, insufficient or poor-quality sleep and chronologically disorganized meals are additional factors that disrupt glucose homeostasis through distinct hormonal and circadian mechanisms^(1,4,5).

Early identification of modifiable risk factors for insulin resistance among young adults represents a public health priority, given that lifestyle interventions can significantly prevent or delay progression to type 2 diabetes mellitus and its cardiometabolic complications^(1,2).

In this context, the present study aimed to evaluate the associations between dietary habits, physical activity level, sleep quality and anthropometric markers of metabolic risk in a sample of young adults aged 18 to 35 years old.

Materials and method

Study design

The study followed a cross-sectional design, made in 2026, based on data collection through a structured online questionnaire comprising 30 items. The variables analyzed included anthropometric data, dietary habits (including consumption of high glycemic index foods, meal regularity, and eating speed), symptoms suggestive of glycemic fluctuations, sleep quality, physical activity level and family history.

Study population and eligibility criteria

The study included 191 Romanian participants aged between 18 and 35 years old, recruited through the online distribution of the questionnaire. Inclusion criteria comprised: age within the target range, informed consent to participate and complete questionnaire completion, including the provision of essential anthropometric data (weight, height). Incomplete questionnaires, those with inconsistent responses, or those lacking anthropometric data were excluded.

The gender distribution revealed a predominance of female participants (77%) compared to male participants (23%). This uneven distribution reflected a well-documented trend in online health-related questionnaire studies, in which women participate more frequently.

Anthropometric indicators

Body Mass Index was calculated using the standard formula: BMI = weight (kg)/height² (m²). Based on BMI values, the participants were classified according to World Health Organization criteria as: normal weight (BMI 18.5-24.9 kg/m²), overweight (BMI 25-29.9 kg/m²) and obese (BMI≥30 kg/m²). Waist circumference was used as an additional marker of central obesity, recognized as an independent predictor of insulin resistance.

Statistical analysis

Collected data were compiled and analyzed using descriptive statistical methods (means, standard deviations, frequencies, percentages) and inferential tests. Associations between categorical variables were assessed using the Chi-square test, with the statistical significance threshold set at p

Results

General characteristics of the study population

Out of the 191 participants included in the analysis, 50.8% were under 25 years of age, 42.4% were 25-29 years old, and 6.8% were 30-35 years old. The distribution by BMI category showed that 62.3% of participants were of normal weight, 25.1% were overweight, and 12.6% were obese, meaning that 37.7% of the sample exceeded normal weight, a proportion directly relevant to the analysis of metabolic risk (Figure 1).

Association between BMI and history of elevated blood glucose (p=0.001)

A statistically significant association was identified between BMI category and history of elevated blood glucose (χ²; p=0.001). Participants with obesity most frequently reported a prior episode of elevated blood glucose (44.4%), compared to those who were overweight (22.2%) and of normal weight (33.3%). These data confirm that obesity as an independent risk factor for impaired glucose metabolism in young adults (Figure 2).

Association between family history of type 2 diabetes and elevated blood glucose

The presence of a family history of type 2 diabetes mellitus was significantly associated with a personal history of elevated blood glucose (χ²; p=0.030). Respondents with first-degree relatives with type 2 diabetes mellitus reported a prior episode of elevated blood glucose in 66.7% of cases, compared to 33.3% among those without a positive family history. This association confirms the role of genetic predisposition as a non-modifiable risk factor, with direct implications for the need for early metabolic screening (Figure 3).

Association between dietary pattern and irregular meal timing

The frequency of irregularly timed meals revealed a highly significant association with the overall dietary pattern (χ²; p<0.001). Participants whose diet was predominantly based on processed and fast food reported eating at irregular hours daily in 58.3% of cases, compared to 9.9% among those with a balanced diet. Day-to-day variable eating patterns were correlated with frequent irregular meals (63.3%), suggesting that dietary disorganization has a direct impact on glycemic rhythm (Figure 4).

Association between postprandial hunger and post-meal fatigue

A statistically significant association was identified between the presence of postprandial drowsiness/fatigue and feeling hungry shortly after eating (χ²; p=0.001). Among participants reporting frequent postprandial hunger, 61.7% also experienced constant post-meal fatigue, compared to 31.9% of those who did not report postprandial hunger. The co-occurrence of these two symptoms signals possible postprandial glycemic fluctuations, a compensatory mechanism characteristic of the hyperinsulinism seen in subclinical insulin resistance (Figure 5).

Additionally, descriptive analysis revealed that 52.3% of participants spend more than six hours per day in a sedentary position, and 52.3% reported fluctuating or poor sleep quality. Although these factors did not reach statistical significance in the associations tested, likely due to design limitations, they remain relevant in the context of international literature and are discussed in the following section.

Discussion

Interpretation of results in pathogenic context

The four significant associations identified in this study are consistent with the known pathogenic mechanisms of insulin resistance and with trends documented in international literature.

The first association, obesity and elevated blood glucose (p=0.001), is supported by the molecular mechanisms through which visceral adipose tissue drives hypersecretion of proinflammatory adipokines, mitochondrial oxidative stress and insulin receptor dysfunction⁽¹⁾. The current data align with the trend documented by Parcha et al. (2022), who demonstrated that insulin resistance affects approximately 40% of American young adults, including 50% with normal BMI, an observation consistent with the 33.3% rate of elevated blood glucose episodes reported by normal-weight participants in our study⁽²⁾.

The second association, family history of type 2 diabetes and elevated blood glucose (p=0.030), confirms the amplification of genetic susceptibility by environmental factors. Genetic predisposition acts through a reduced number or affinity of insulin receptors, polymorphisms in intracellular signaling pathways and altered pancreatic beta-cell function, mechanisms documented in recent pathogenic reviews⁽¹⁾.

The third association, disorganized dietary pattern and irregular meal timing (p<0.001), adds a chronobiological dimension to the analysis. Santos-Báez et al. (2024) demonstrated, through continuous glucose monitoring, that time of day, meal composition and meal regularity significantly affect postprandial glycemic variability⁽⁶⁾. Ali et al. (2023) confirmed, in a nationally representative US sample, that meal timing and eating-window duration independently influence fasting glucose and insulin levels, regardless of dietary composition⁽⁴⁾. Nakamura et al. (2024) documented that late dinner (after 9:00 PM) significantly raises postprandial glucose and prolongs the insulin response, even in healthy young adults⁽⁷⁾; in our sample, 18.8% reported eating dinner after 9:00 PM, placing this cohort in the risk category described.

The fourth association, postprandial hunger and post-meal fatigue (p=0.001), has clinical relevance due to its semiotic value. The coexistence of these two symptoms suggests postprandial reactive hypoglycemia or wide glycemic variability, mechanisms characteristic of compensatory hyperinsulinism. Park et al. (2025) showed that similar behavioral patterns are associated with metabolic subphenotypes that precede type 2 diabetes by years before diagnosis, underscoring the importance of early recognition of these symptoms at the subclinical stage⁽⁸⁾.

Sedentary behavior and sleep factors of epidemiological relevance

Although sedentary behavior and sleep quality did not reach statistical significance in the associations tested, the descriptive data from this study are concerning: 52.3% of participants spend more than six hours per day in a static position, and 9.4% sleep less than six hours per night.

Cavallo et al. (2022), in a meta-analysis of 45 studies, concluded that sedentary behavior is consistently and negatively associated with insulin sensitivity and the incidence of type 2 diabetes⁽⁹⁾. Gupta et al. (2026) quantified that more than 8-10 hours of daily sedentary time increases the odds of insulin resistance by 33% (OR 1.33; 95% CI; 1.12-1.57)⁽¹⁰⁾. The lack of statistical significance in the present study may be explained by the limited statistical power of the subgroups and by the self-reported nature of the data, which frequently underestimates actual sedentary behavior.

Regarding sleep, Singh et al. (2022) documented that short sleep duration disrupts several biological mechanisms relevant to glucose metabolism: increased inflammatory markers, decreased GLP-1 secretion and circadian misalignment⁽⁵⁾. Pinheiro et al. (2025) further note that sleep deprivation causes a 15-30% increase in circulating free fatty acids overnight, with a direct effect of reducing insulin sensitivity⁽¹¹⁾.

Contribution of carbohydrate quality

Although no statistically significant association was found between consumption of high glycemic index carbohydrates and anthropometric or glycemic parameters, the specialized literature indicates a real effect. Yu et al. (2025), in a meta-analysis of adults without diagnosed diabetes, demonstrated that increased frequency of high glycemic index carbohydrate consumption is independently associated with higher HOMA-IR values⁽³⁾. The lack of significance in our sample may be explained by the self-reported nature of the data and by the small subgroup of participants whose diet was predominantly based on processed foods (n=12), insufficient for robust statistical testing.

Study limitations

The first fundamental limitation relates to the cross-sectional design: capturing a single point in time, it does not allow for the establishment of cause-and-effect relationships, and the temporal order of events remains unknown.

A second limitation arises from the exclusive use of self-reported data, which is subject to social desirability bias and recall errors. The absence of any objective biological measurement, HOMA-IR, fasting blood glucose or oral glucose tolerance test makes clinical confirmation of insulin resistance among participants impossible.

The sample comprised 77% women, recruited predominantly through social media, favoring individuals with access to technology and higher educational attainment, thereby limiting representativeness and the generalizability of the results. The subgroup with a diet based on processed foods and fast food (n=12) was insufficient for robust statistical estimates.

Conclusions

The results of this study confirm that insulin resistance is not the exclusive domain of middle age or individuals with obesity; it is also present among apparently healthy young adults, often in the absence of an explicit diagnosis. The four statistically significant associations identified, obesity and elevated blood glucose (p=0.001), family history of type 2 diabetes and elevated blood glucose (p=0.030), disorganized dietary pattern and irregular meals (p<0.001), and frequent postprandial hunger and post-meal fatigue (p=0.001) outline a multifactorial risk profile in which genetic factors overlap with maladaptive dietary behaviors and chronotypes.

The 18-35 years old age range represents a window of opportunity for preventive intervention: dietary habits are not yet firmly established, and lifestyle changes can significantly prevent or delay the onset of type 2 diabetes mellitus. Future studies should incorporate objective biological measurements (HOMA-IR, fasting blood glucose), a longitudinal design and more representative samples in terms of gender distribution and socioeconomic background.

Conflict of interests: none declared.

Financial support: none declared.

This work is permanently accessible online free of charge and published under the CC-BY.