Association between serum 1,5-anhydroglucitol, dietary energy density and lifestyle factors in women with newly diagnosed type 2 diabetes

Association between serum 1,5-anhydroglucitol, dietary energy density and lifestyle factors in women with newly diagnosed type 2 diabetes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between serum 1,5-anhydroglucitol, dietary energy density and lifestyle factors in women with newly diagnosed type 2 diabetes Tevfik KOÇAK, Eda KÖKSAL, Mujde AKTURK This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8012414/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Objective High dietary energy density (DED) is a recognized risk factor for obesity and type 2 diabetes mellitus (T2DM), whereas balanced diets and adequate physical activity support glycemic regulation. This study investigated serum 1,5-anhydroglucitol (1,5-AG) as a short-term biomarker of glycemic control in women with newly diagnosed T2DM and explored its associations with glycemic markers, DED, and lifestyle factors. Methods Eighty-eight women (44 with T2DM, 44 healthy controls; 45–65 years) participated. Sociodemographic data, medical history, and physical activity were recorded. Anthropometric and body composition measures were obtained, and biochemical parameters (HbA1c, fasting glucose, insulin, cholesterol fractions, triglycerides, C-peptide, and 1,5-AG) were analyzed. Dietary intake was assessed using 3-day dietary records, and DED was calculated as energy (kcal) from foods, excluding beverages, per gram. Results Serum 1,5-AG was significantly lower in T2DM patients (5.03 ± 1.42 µg/mL) compared with controls (13.05 ± 4.96 µg/mL) (p < 0.05). HbA1c, fasting glucose, insulin, C-peptide, triglycerides, and HOMA-IR were significantly elevated in T2DM (p < 0.05). Although total energy intake was similar, patients consumed more protein, fat, polyunsaturated fatty acids, and fiber, while carbohydrate intake and DED were lower (p < 0.05). Correlations emerged between dietary/lifestyle factors and glycemic indicators. In multivariable regression, 1,5-AG was independently associated with HbA1c (p < 0.001), sleep duration (p < 0.05), and DED (p < 0.05). Conclusion Reduced serum 1,5-AG in T2DM supports its utility as a short-term biomarker of glycemic regulation. Its links with HbA1c, sleep, and DED emphasize the interplay between lifestyle and metabolic control. Integrating 1,5-AG into clinical evaluation may enhance individualized management in T2DM. Type 2 diabetes 1 5-anhydroglucitol glycemic control alternative biomarker Figures Figure 1 1. Introduction Type 2 diabetes mellitus (T2DM) is a chronic metabolic condition marked by sustained hyperglycemia due to decreased insulin production, insulin resistance, or a combination of both factors. The global incidence has been increasing significantly, primarily due to lifestyle-related variables like aging, gender disparities, poor dietary habits, obesity, physical inactivity, and changes in circadian rhythms resulting in sleep disorders. [ 1 ]. Since 1990, the number of individuals living with T2DM has increased substantially, reaching approximately 828 million by 2022 [ 2 ], and this figure is projected to climb further, with the International Diabetes Federation (IDF) estimating 589 million adults with diabetes in 2024 and 853 million by 2050. This dramatic rise poses a major public health and economic challenge worldwide [ 3 ]. In Türkiye, the burden is equally alarming: as of 2021, nearly 9 million individuals had been diagnosed with T2DM [ 4 ], and the prevalence is expected to rise from 16.5% in 2024 to 18.6% by 2050, ranking the country among those with the highest diabetes prevalence in the European region. Nutrition is crucial in the development of T2DM and in attaining glycemic control [ 5 ]. In adults, dietary patterns characterized by high caloric density and shortened meal intervals are strongly associated with weight gain, obesity, and an elevated risk of T2DM [ 6 ]. Evidence further suggests that energy-dense diets reduce insulin sensitivity and increase glycemic variability [ 7 , 8 ]. Importantly, alterations in dietary composition and meal timing can modulate the expression and activity of enzymes involved in cholesterol, amino acid, lipid, glycogen, and glucose metabolism, thereby influencing overall energy homeostasis [ 9 ]. Dietary alterations may lead to negative metabolic consequences, such as obesity, dyslipidemia, insulin resistance, and hypertension, ultimately disrupting postprandial glycemic control. [ 10 ]. Attaining consistent long-term glycemic regulation is crucial to mitigating problems associated with T2DM and related chronic diseases. [ 11 ]. Frequently utilized indicators encompass glycated hemoglobin (HbA1c), fasting glucose, and alternative biomarkers like 1,5-anhydroglucitol (1,5-AG), each representing glycemic state throughout varying temporal spans. [ 12 – 15 ]. Although HbA1c reflects average glucose levels over the past 2–3 months, it fails to sufficiently account for glycemic variability, including postprandial hyperglycemia or hypoglycemia over this timeframe. [ 16 ]. Furthermore, due to the gradual alteration of HbA1c levels post-therapeutic interventions, biomarkers that can swiftly and reliably indicate glycemic variations, exhibit metabolic stability, possess low biological variability, and are readily measurable provide significant benefits in diabetes management. [ 17 ]. In this context, serum 1,5-AG has emerged as a valuable short-term biomarker that addresses many of these limitations [ 18 ]. 1,5-AG is a naturally occurring monosaccharide occurring monosaccharide in various foods foods, with its circulating levels maintained primarily through reabsorption in the renal proximal tubules [ 19 ]. Increased urine glucose excretion leads to competition between glucose and 1,5-AG for reabsorption, resulting in diminished tubular reuptake and decreased serum concentrations. Consequently, reduced serum 1,5-AG is acknowledged as a clinically significant indicator of postprandial hyperglycemia [ 20 ]. Importantly, serum 1,5-AG reflects glycemic status over the preceding 1–2 weeks, offering a short- to intermediate-term assessment that provides advantages over conventional markers in capturing postprandial glycemic excursions [ 21 – 23 ]. Nonetheless, despite increasing data on its clinical relevance, there is insufficient research examining how dietary patterns and lifestyle factors affect blood 1,5-AG levels, highlighting a significant gap in comprehending its wider role in glycemic regulation. Nutrition and lifestyle are pivotal in the development and control of Type 2 Diabetes Mellitus (T2DM). Traditional indicators like HbA1c and fasting glucose are prevalent yet inadequate for identifying short-term glycemic fluctuation. Serum 1,5-AG has emerged as a viable alternative, providing a more sensitive indication of recent hyperglycemia variations. Nonetheless, its association with dietary energy density, nutritional status, and lifestyle variables is inadequately investigated. This study aims to investigate the relationships between serum 1,5-AG, conventional glycemic indicators, and specific dietary and lifestyle factors in persons with newly diagnosed T2DM. 2. Methods Study design and participants This case–control study was conducted between September 2017 and April 2018 at Gazi University Hospital, Ankara, Türkiye. Participants in the case group were recruited from the Diabetes and Obesity Clinic within the Department of Endocrinology, whereas controls were enrolled through the Dietetics Clinic of the Department of Nutrition and Dietetics. The study population comprised 44 female patients newly diagnosed with T2DM and 44 age-matched healthy female volunteers, all between 45 and 65 years of age. Sample size was calculated a priori using power analysis, which indicated that a minimum of 40 participants per group was required to achieve 90% statistical power with a 5% margin of error. The exclusion criteria for cases included a diabetes duration exceeding 51 years, a daily caloric intake below 600 kcal/d or above 4000 kcal/d, a diabetes duration surpassing two years, HbA1c levels outside the range of 6.5–8.5%, and the existence of chronic comorbidities such as renal, hepatic, parathyroid, thyroid, or cardiovascular diseases, hypertension, cancer, osteomalacia, chronic diarrhea, or malabsorption. Individuals with recorded sleep difficulties, mental illnesses, or microvascular and macrovascular consequences of type 2 diabetes mellitus were likewise eliminated. Furthermore, individuals who have undergone insulin therapy or pharmacological interventions, including oral antidiabetic agents excluding metformin, corticosteroids, vitamin-mineral supplements, osteoporosis medications, postmenopausal hormone therapy, or vitamin D supplementation in the preceding six months were deemed ineligible. Cases and controls were excluded if they indicated alcohol consumption, pregnancy, or lactation. The data for the study were collected using face-to-face interviews with a researcher-designed questionnaire encompassing sociodemographic and health information, anthropometric measures, body composition, biochemical data, and a 3-day dietary record. The study protocol was conducted according to the principles of the Declaration of Helsinki. Ethical permission was secured by the Ethics Committee of Gazi University Hospital (Decision No: 24074710-29), and informed consent was acquired from all participants. The flow chart of the study is presented in Fig. 1. Anthropometric Measurements Body weight, fat-free mass, body fat percentage, and body water percentage were assessed utilizing a portable body composition analyzer (Tanita BC 601). Height, waist circumference, and neck circumference were measured with an accuracy of 0.1 cm, while weight was measured with an accuracy of 0.1 kg [24]. The body mass index (BMI) was computed by dividing weight (kg) by height squared (m²) [25]. Waist circumference was measured with the individual standing, arms hanging at the sides, and legs together. The midpoint between the lowest rib and the cristae was marked using a non-stretchable tape measure, ensuring the tape measure was parallel to the floor without compressing the tissue. [26]. The waist-to-height ratio and neck circumference were derived from waist and height measurements. These statistics are utilized to assess an individual's risk of chronic disease [27, 28]. Biochemical Analyses Fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using a Beckman Coulter analyzer. Insulin was analyzed via chemiluminescence (Beckman Coulter DxI). HbA1c was measured using the VARIANT II TURBO analyzer. C-peptide was measured using a Siemens IMMULITE 2000 analyzer. The reference range for 1,5-AG was 0–30.21 µg/mL. To assess insulin sensitivity, the "homeostasis model assessment for insulin resistance" (HOMA-IR) calculation was performed using the formula "fasting blood glucose (mmol/L) x fasting insulin (µU/mL)/22.5." Values of "≤2.7" and ">2.7" were used for the HOMA-IR value [29]. 1,5-Anhydroglucitol Measurement Blood samples for 1,5-AG and glycated albumin were collected after 12 hours of fasting, centrifuged at 3000 rpm for 15 minutes, and stored at − 80°C. Analyses were performed using the Mybiosource MBS012290 ELISA Kit in the Diagen Biotechnology Laboratory. Assessment of Physical Activity The nature and duration of activities undertaken by the individuals before the interview were documented on the activity record form utilizing a reminder technique. To determine the energy expended during physical activity, the documented durations of activities were multiplied by the energy costs (Physical Activity Ratio - PAR) [ 24 ] based on the nature of the activity and the individuals' resting metabolic rate, as measured by a MedGem® Indirect calorimeter, to compute total energy expenditure (TEE). The durations derived from these activities (PAR values) were divided by 24 hours/1440 minutes to get the individual's daily Physical Activity Level (PAL) [ 24 ]. Resting Metabolic Rate The resting metabolic rate (RMR) was assessed utilizing the MedGem® indirect calorimeter. Measurements were conducted during a minimum rest period of 15–20 minutes in a tranquil, temperature-regulated environment. Each measurement endured for around 8 to 10 minutes. [ 30 ]. Assessment of Circadian Rhythm The Morningness-Eveningness Questionnaire (MEQ) was used to determine chronotype (morning, evening, or intermediate). The Turkish adaptation of the MEQ was validated by Pündük et al. in 2005 [ 31 ]. Scores classified participants into five chronotypes: definite morning type (70–86), moderately morning type (59–69), intermediate type (42–58), moderately evening type (31–41), and definite evening type (16–30). Lower scores have been linked to obesity and chronic disease [ 31 ]. Assessment of Stress The Depression, Anxiety, and Stress Scale (DASS-21R) is a commonly utilized instrument for assessing levels of depression, anxiety, and stress. This scale has 21 items designed to assess the severity of psychological symptoms and offers insights into the levels of depression, anxiety, and stress across three primary dimensions. The DASS-21R is extensively utilized to evaluate the severity of various diseases [ 32 ]. The DASS-21R questionnaire has significant usefulness in research and clinical environments, with the depression (α = 0.83–0.94), anxiety (α = 0.66–0.87), and stress (α = 0.79–0.91) subscales reflecting robust internal consistency [ 33 ]. The 21-item abbreviated Turkish version of the Depression, Anxiety, and Stress Scale has demonstrated validity and reliability [ 34 ]. Assessment of Dietary Intake To assess and calculate the energy and nutrient intake of the individuals participating in the study, the researcher recorded three consecutive days of food consumption, two on weekdays and one on the weekend. At the beginning of the study, the researcher explained to each patient how to record food consumption, including examples, and provided a three-day record form for recording food intake along with meal times. The amounts of food or meals consumed, to be recorded on the food record form, were explained to the individual using the "Food and Nutrition Photograph Catalog." [ 35 ]. Standardized recipes and portion sizes were derived from “Standard Meal Recipes”[ 36 ], “Selections from Traditional Turkish Cuisine”[ 37 ] and “Examples from Turkish Cuisine”[ 38 ]. Energy and nutrient intakes were computed utilizing the Nutrition Information System (BeBis) version 7.2 [ 39 ]. Calculation of Dietary Energy Density and Intermeal time Two different approaches were used to calculate dietary energy density, as used by Raynor et al. [ 40 ] the first included all foods and all caloric beverages (solid foods + caloric beverages), and the second included all solid foods but excluded beverages (solid foods only). Fruit juices, alcoholic and non-alcoholic beverages, milk beverages, etc., were classified as beverages, while soups, yogurt, ice cream, and other similar products were classified as solid foods [ 41 ]. Density was calculated based on total daily food weight relative to total daily energy intake and recorded in kcal/gram. Dietary energy density was determined for three days, and then the average of the three days was calculated to determine each participant's average daily dietary energy density [ 42 ]. Intermeal intervals are the time difference between one main meal and the next. This time period can have a direct impact on an individual's metabolic status, nutritional habits, and energy needs.[ 43 ]. Statistical Analysis For the statistical evaluation of the data, the Statistical Package for the Social Sciences (SPSS, version: 22.0) statistical package program was used. Mean, standard deviation, median, minimum, and maximum values were used for continuous (quantitative) variables obtained through measurement, while frequency and percentage values were used for categorical (qualitative) variables. A normality test was performed before all data were analyzed. The data's compliance with normal distribution was examined using visual (histogram and probability plots) and analytical methods (Kolmogorov-Simirnov test). Student's t-test was applied to two-group data with normal distribution, and the Mann-Whitney U test, a non-parametric test, was applied to data without normal distribution. Homogeneity was assessed according to the Levene test statistic before applying the Student's t-test. In determining the correlation between numerical data, the Pearson correlation coefficient was used when both variables were normally distributed, and the Spearman correlation coefficient was used when at least one of the two variables was not normally distributed. The results were evaluated at a 95% confidence interval and a statistical significance level of p < 0.05. 3. Results Table 1 summarizes the distribution of anthropometric characteristics, biochemical parameters, and lifestyle variables of the participants. The mean age of participants in the patient group was 53.16 ± 5.13 years, compared to 49.32 ± 3.73 years in the control group. The mean BMI values were 33.17 ± 6,16 kg/m² and 32.45 ± 5,09 kg/m² (p>0.05). While other biochemical indicators except TC, HDL-C and LDL-C; HbA1c, FPG, insulin, TG, HOMA-IR, C-peptide and 1.5-AG levels were significantly higher in the patient group than in the control group, 1.5-AG levels were significantly lower in the patient group than in the control group. Literature findings consistently demonstrate an inverse association of 1,5-AG with hyperglycemic regulation of HbA1c in diabetes, stronger associations with postprandial peaks and CGM variability measures compared to fasting blood glucose, and lower 1,5-AG in diabetes compared to controls. Therefore, our finding supports the value of 1,5-AG as a short-term marker of dysglycemia in patients with newly diagnosed type 2 diabetes. (p < 0.05). MEQ scores were significantly higher in the patient group compared to the control group (p = 0.050), and a greater tendency toward morning chronotype was detected among patients. Table 1. Distribution of general characteristics, biochemical, and lifestyle variables of participants in here Table 2 delineates the distributions of individuals' daily calorie, nutrient, and DED consumption. Upon examination of the energy and macronutrient intakes of the patient and control groups, no statistically significant differences were seen in nutrient intake, excluding protein, carbohydrate, fat, polyunsaturated fatty acids (PUFAs), and fiber (p > 0.05). The patient group had a markedly elevated intake of protein, carbohydrates, polyunsaturated fatty acids (PUFA), and fiber compared to the control group (p 0.05), the patient group exhibited a statistically significant reduction in DED from solid foods compared to the control group (p < 0.05). Table 2. Comparison of energy and nutrient intake between patient and control groups in here Table 3 delineates the connections among anthropometric indicators, biochemical parameters, lifestyle variables, and glycemic control indicators in both the patient and control groups involved in the study. The BMI, waist circumference, waist-to-height ratio, and HDL-C levels of the patient group in the study exhibited a significant correlation with HOMA-IR levels (BMI r=0.450; waist circumference r=0.402; waist-to-height ratio r=0.321; HDL-C r=-0.363). Additionally, BMI, waist circumference, and HDL-C levels demonstrated a significant correlation with insulin levels (BMI r=0.407; waist circumference r=0.369; HDL-C r=-0.324) (p<0.05). Significant relationships were observed between anthropometric indicators, lifestyle variables, and specifically, 1.5-AG demonstrated a significant negative association with anthropometric measurements of waist circumference and waist-to-height ratio (p<0.05). Table 3. Association of glycemic control markers with biochemical data, anthropometric measurements, and lifestyle factors of participants in here Table 4 summarizes the distribution of the correlation of participants' nutrient intakes with glycemic control indicators. In the control group, longer intermeal time was positively correlated with 1,5-AG levels, indicating that longer intervals between meals may support better short-term glycemic control (r = 0,441, p = 0,003). No significant associations were observed in the patient group (p>0.05). In the control group, energy intake correlated positively with insulin (r = 0,299, p = 0,048), HOMA-IR (r = 0,347, p = 0,021), and C-peptide (r = 0,336, p = 0,026), suggesting higher energy intake is linked to insulin resistance and β-cell activity. In the control group, carbohydrate (r = 0,305, p = 0,047) and protein intake (r = 0,300, p = 0,048) correlated positively with HbA1c, indicating a possible link between higher protein intake and poorer long-term glycemic control. In the patient group, positive correlations with insulin (r = 0,337, p = 0,025) and HOMA-IR (r = 0,361, p = 0,016), suggesting higher saturated fatty acids intake may worsen insulin resistance. In the control group, monounsaturated fatty acids were positively correlated with insulin r = 0,305, p = 0,047) and HOMA-IR (r = 0,300, p = 0,048). Fiber intake showed no significant associations with glycemic indicators in either group (p>0.05). Cholesterol intake correlated positively with C-peptide in the control group (r = 0,374, p = 0,048). In the control group, omega-3 intake correlated negatively with HbA1c (r = -0,313, p = 0,039)and positively with 1,5-AG (r = 0,300, p = 0,048), indicating a protective role against poor glycemic control (p0.05). Table 4. Correlation of participants nutrient intakes with glycemic control indicators in here Table 5. Multiple linear regression model for the relationship between 1,5 Anhidroglusitol and some variables of diabetic patients details the regression model. Multiple linear regression analysis was conducted to evaluate the relationship between 1,5-AG, HbA1c, FPG, sleep duration, DASS score, MEQ score, and DED in the patient group. The model was significant (R² = 0.615; p<0.001). The strong model fit (R² = 0.615) indicates that such a multidimensional framework is both clinically meaningful and methodologically robust, suggesting potential for 1.5-AG to be applied not only as a diagnostic adjunct but also as a lifestyle-sensitive marker for personalized nutrition interventions in diabetes management. Significant predictors of 1,5-AG levels were HbA1c ( B :-1,481; p < 0.001), sleep duration (p<0.05), and DED (p<0.05). Table 5. Multiple linear regression model for the relationship between 1,5 Anhidroglusitol and some variables of diabetic patient in here 4. Discussion The primary emphasis of our investigation was the association between blood 1,5-AG concentration and dietary habits, anthropometric measurements, diabetes-related biochemical markers, and lifestyle variables. The content of serum 1,5-AG exhibited a substantial connection with anthropometric factors, including BMI and waist circumference. Furthermore, the content of 1,5-AG exhibited a strong correlation with omega-3 consumption. Linear regression analysis demonstrated an inverse linear correlation between 1,5-AG levels and FPG and HbA1c concentrations in the diabetic cohort. The findings indicate that serum 1,5-AG levels diminish as FPG and HbA1c levels rise in individuals with diabetes. Furthermore, the identified correlations between 1,5-AG levels and DED and sleep, as nutritional and lifestyle determinants, may hold significance for future investigations. The study's strength is its demonstration of the utility of 1,5-AG as an alternative additional biomarker for evaluating short-term glycemic control in individuals with type 2 diabetes. The main limitation of our study is that the correlations of 1,5-AG levels were assessed only in female patients. Dietary behavior modification, physical activity, and lifestyle changes play an effective role in developing and preventing T2DM. Individualized dietary energy restriction, consumption of fiber-rich foods, careful planning of meal content and timing, adequate and balanced intake of whole grains, vitamins, and minerals, as well as limiting the intake of saturated fats and foods increasing T2DM risk, not only reduce the risk of developing T2DM but also play an active role in achieving glycemic control in obese patients with T2DM [ 44 – 46 ]. Sustained long-term tight glycemic control is critically important for preventing complications of T2DM and associated chronic conditions [ 47 , 48 ]. Among the main parameters for monitoring glycemic control, HbA1c, fasting, and postprandial plasma glucose values are considered most important; HbA1c, in particular, is closely related to complications and is used as the gold standard for assessing glycemic control [ 49 , 50 ]. Along with these parameters, the use of continuous glucose monitoring systems and 1,5-AG has been recommended to better monitor hypo- and hyperglycemic episodes [ 12 , 51 , 52 ]. The advantage of 1,5-anhydroglucitol lies in its ability to reflect short-to-intermediate glycemic fluctuations and in its independence from hemoglobin metabolism, making it a promising new indicator for postprandial blood glucose levels [ 53 , 54 ]. he 2011 International Diabetes Federation (IDF) guidelines reported that 1,5-AG could be a more effective indicator for glycemic control by more accurately reflecting short-term glycemic variability and postprandial hyperglycemia [ 55 , 56 ]. In our study, HbA1c, FPG, serum insulin, triglyceride, HOMA-IR, and C-peptide levels of the patient group were significantly higher (p < 0.05), while 1,5-AG levels were significantly lower (p < 0.05) than those of the control group (Table 1 ). These findings support the hypothesis that increased blood glucose levels lead to increased renal tubular excretion of 1,5-AG, resulting in decreased plasma levels. Interestingly, no significant differences were observed in BMI, waist circumference, or body fat percentage between groups, suggesting that biochemical dysregulation may precede or outweigh overt anthropometric differences in the early phase of T2DM. In addition, it is thought that the high levels of HbA1c, FPG, serum insulin levels, triglyceride levels, HOMA-IR, and C-peptide levels, which are due to the low compliance of patients with medical nutrition therapy and the lack of glycemic control of individuals, support the biochemical changes that occur after diabetes in the literature. In individuals with diabetes, an adequate and balanced dietary pattern, along with appropriate meal timing, is grounded in concepts derived from clinical research, portion control, and personalized lifestyle modifications [ 57 ]. Effective nutritional management is essential to comprehensive treatment for individuals with T2DM. Maintaining a regular number of meals, appropriate intervals between meals, and an optimal meal pattern plays a critical role in achieving glycemic control [ 58 ]. To ensure postprandial glycemic control, it is crucial to select foods that promote the maintenance of normal blood glucose levels. Dietary carbohydrates are the primary macronutrients influencing postprandial glycemic responses. However, other macronutrients, including fat, protein, and dietary fiber, can also significantly affect two-hour postprandial glucose (PPG) levels [ 59 ]. One study demonstrated that nutrient intake in the T2DM group—specifically potassium, calcium, magnesium, zinc, iodine, carotene, vitamin D, tryptophan, and vitamin B 12 —was inversely correlated with diabetes incidence, while carbohydrate and protein intake were significantly higher in this group [ 60 ]. Similarly, a prospective cross-sectional study conducted in Tunisia, which compared individuals with diabetes, prediabetes, and healthy controls, found that the diabetic group consumed hypercaloric diets high in carbohydrates and fats. In this group, elevated intake of vitamin A and sodium, along with reduced protein intake, was associated with impaired glucose homeostasis [ 61 ]. Irregular meal frequency and suboptimal intervals between meals can substantially increase the body’s allostatic load, leading to disturbances in glucose regulation and potentially contributing to the development of insulin resistance [ 62 ]. The concept of chrononutrition, which recognizes that meal timing—alongside food quality and quantity—plays a critical role in health, emphasizes the alignment of meals with circadian rhythms [ 63 ]. Frequent consumption of high–dietary energy density (DED) meals, typically composed of refined grains, added sugars, trans fats, and high-fructose corn syrup, has been linked to increased prevalence of obesity and related diseases [ 64 ]. Furthermore, another study reported that liquid-form dietary patterns generally have a lower glycemic index (GI) and insulin index compared to solid-form diets, while solid foods lead to faster increases in blood glucose and insulin levels than their liquid counterparts [ 65 ]. Contrary to expectations, in our study, total energy intake did not differ significantly between patients and controls (p < 0.05). However, dietary macronutrient distribution revealed notable distinctions: the T2DM group (2.21 ± 0.39 g/kcal) consumed higher proportions of protein and fat, particularly polyunsaturated fatty acids, and lower carbohydrate intake and DED from solid foods (p < 0.05) (Table 2 ). These dietary shifts may reflect conscious efforts toward carbohydrate restriction following diagnosis, in line with clinical recommendations for glycemic management [ 66 ]. The increased consumption of protein and fat highlights the intricacies of dietary modifications in diabetes and prompts inquiries about the long-term cardiometabolic consequences of macronutrient replacement. This finding may be explained by the fact that the participants in the T2DM group were newly diagnosed and had not yet fully adapted to the medical nutrition therapy prescribed after diagnosis, which—combined with higher intakes of protein, carbohydrates, PUFAs, and fiber—may have resulted in a lower DED from solid foods. Notably, fiber consumption did not differ significantly between groups; however, regression and correlation analyses indicated that increased dietary fiber may moderate glucose homeostasis, aligning with previous evidence connecting fiber to enhanced insulin sensitivity and PPG regulation [ 67 ]. The diminished DED noted in patients may signify healthier dietary selections; nonetheless, its correlation with decreased 1,5-AG levels implies that overall dietary quality and nutrient equilibrium are essential. Obesity is a major risk factor for T2DM, and body weight, height, and waist circumference (WC) can be easily assessed using cost-effective tools. However, BMI has certain limitations, as it does not differentiate between fat mass and lean body mass, which can lead to the misclassification of individuals with high muscle mass as overweight or obese. Although WC provides a more direct measure of central obesity, it is still insufficient for distinguishing between subcutaneous and visceral fat, which are associated with different metabolic risks [ 68 ]. In one study, reductions in BMI, body weight, and WC achieved through pharmacotherapy were significantly associated with improvements in glycemic control parameters [ 69 ]. Another study found that increased WC in women was associated with poorer HbA1c control and lipodystrophy [ 70 ]. Dyslipidemia frequently coexists with impaired glucose metabolism. In addition to conventional lipid parameters—TG, TC, HDL-C, and LDL-C—non-traditional lipid parameters such as TG/HDL-C, LDL-C/HDL-C, non-HDL-C, TC/HDL-C, and non-HDL-C/HDL-C are strongly associated with the onset and progression of prediabetes and type 2 diabetes [ 71 ]. Several studies have reported correlations between blood glucose levels and serum lipids or lipid ratios, with glucose levels positively associated with TG, LDL-C, and elevated TG/HDL-C and LDL-C/HDL-C ratios [ 72 , 73 ]. In our study, insulin and HOMA-IR values—two glycemic control parameters—were significantly correlated with BMI, WC, waist-to-height ratio, and HDL-C levels in the T2DM group (p < 0.05). In the control group, nearly all glycemic control parameters founded significant correlations with anthropometric variables (BMI, WC, waist-to-height ratio) and lipid parameters (TC, HDL-C, and TG) (p < 0.05) (Table 3 ). These findings support the importance of maintaining optimal cut-off values for anthropometric measures and lipid levels in both T2DM patients and healthy individuals to preserve glycemic regulation. Circadian rhythm disruption has been linked to increased risks of metabolic disorders, T2DM, cardiovascular disease, obesity, and poor sleep quality [ 74 ]. One study examining the interaction between circadian rhythm and chrononutrition demonstrated that altering meal timing reduced resting energy expenditure before meals, did not change postprandial energy expenditure, decreased fasting carbohydrate oxidation, impaired glucose tolerance, blunted the diurnal profile of free cortisol concentrations, and reduced the thermic effect of food [ 75 ]. Globally, poor sleep quality is a common problem among individuals with T2DM, affecting up to half of this population. Poor sleep quality can exacerbate complications by impairing glucose metabolism, increasing inflammation, altering hormonal regulation, and lowering quality of life. Therefore, improving sleep quality has become a key goal in diabetes management [ 76 ]. Studies have suggested that physical activity could be a primary focus in interventions aimed at improving sleep quality in T2DM populations [ 77 ]. Compared to non-diabetic individuals, patients with type 1 and type 2 diabetes have been reported to have higher resting metabolic rates (RMR), which may be attributed to increased catabolic rates, hyperglucagonemia, and enhanced gluconeogenic activity [ 78 ]. Evidence from previous research strongly supports the benefits of aerobic exercise in improving glycemic control among individuals with T2DM, emphasizing the importance of incorporating regular physical activity into diabetes management strategies to optimize health outcomes and reduce complication risks [ 79 ]. In our study, no significant correlations were found between lifestyle variables (energy expenditure from physical activity, physical activity level, RMR, sleep duration, total stress score, and circadian rhythm score) and glycemic control indicators in the T2DM group (p > 0.05). However, in the control group, FPG was significantly correlated with circadian rhythm score, insulin and HOMA-IR were correlated with RMR, and C-peptide was correlated with physical activity variables (p < 0.05) (Table 3 ). These findings highlight the importance of lifestyle and anthropometric factors in glycemic regulation. Lifestyle characteristics, including physical activity, sleep duration, and circadian rhythm scores, exhibited minimal differences between groups; nonetheless, regression modeling indicated substantial correlations. The duration of sleep was favorably correlated with 1,5-AG, reinforcing the increasing acknowledgment of sleep as a factor influencing metabolic health.Interestingly, stress scores did not correlate with 1,5-AG, although psychological stress has been implicated in dysregulated glucose metabolism through neuroendocrine pathways [ 80 ]. The lack of significance in our study may be due to sample size or the cross-sectional design, limiting temporal inference. A systematic review of 102 randomized controlled trials investigating the effects of altering dietary fat and carbohydrate composition on blood glucose control, insulin sensitivity, and insulin secretion found that replacing dietary carbohydrates with saturated fat did not significantly affect glycemic control indicators. However, replacing carbohydrates and saturated fat with unsaturated fats—particularly PUFAs—was found to benefit blood glucose control, insulin sensitivity, and insulin secretion [ 81 ]. In a study by Fujii et al. [ 82 ] examining the relationship between fatty acids and insulin resistance in Japanese individuals, the percentage of linoleic acid intake was negatively correlated with visceral adipose tissue, fasting glucose, HbA1c, HOMA-IR, and systolic blood pressure. Similarly, Wanders et al. [ 83 ] reported a positive association between saturated fatty acid intake (compared to carbohydrates) and fasting insulin and HOMA-IR levels. Additionally, total fatty acid intake from meat and meat products was positively associated with fasting insulin levels. In another study by Nigam et al. [ 84 ] investigating plasma n-3, n-6, and PUFA levels and insulin resistance in individuals with metabolic syndrome, HOMA-IR was positively correlated with total saturated fatty acid and n-6 fatty acid levels, while negatively correlated with total n-3 fatty acid levels. Moreover, total n-3 and n-6 fatty acid levels and the n-6/n-3 ratio were found to be associated with HOMA-IR levels. Consistent with these findings, our study also revealed a positive and significant correlation between dietary saturated fat intake percentage and HOMA-IR values in the T2DM group (p < 0.05). In the control group, significant positive correlations were found between omega-3 intake and 1,5-anhydroglucitol, cholesterol intake and FPG, protein, carbohydrate, and trans fatty acid intake and HOMA-IR, as well as trans fatty acid intake and insulin levels (p < 0.05) (Table 4 ). Similarly, in our study, a positive and significant correlation was found between saturated fat intake and HOMA-IR values ​​in the diabetic group (p < 0.05). In the control group, a positive and significant correlation was found between dietary omega-3 intake and 1.5 AG, cholesterol intake and FPG, protein, carbohydrate, and monounsaturated fatty acid intake and HOMA-IR, and TSFA intake and insulin levels. (p < 0,05) (Tablo 4). This indicates that macronutrient consumption is directly or indirectly associated with markers indicative of glycemic control. Diabetes, particularly when complicated by obesity, represents a major health concern in modern society. Dietary planning should therefore consider individual characteristics such as age, sex, and BMI, tailoring macronutrient and micronutrient intake to the individual. Experimental studies have shown that high–dietary energy density meals increase PPG levels, free fatty acid concentrations, and insulin resistance [ 85 , 86 ]. However, in our study, no statistically significant relationship was found between biochemical parameters and dietary energy density or meal interval duration in the T2DM group (p > 0.05). In the control group, HbA1c levels were positively correlated with meal interval duration (p < 0.05) (Table 4 ). This finding may be explained by the higher DED in the control group compared to the T2DM group, where increased DED could induce glycemic fluctuations, thus leading to a positive correlation with HbA1c. Interest in 1,5-anhydroglucitol (1,5-AG) as an alternative to blood glucose or HbA1c is increasing. A short-term glycemic marker sensitive to glycemic fluctuations may provide valuable and timely feedback in diabetic patients following therapeutic and dietary interventions, even before changes in HbA1c become apparent [ 87 ]. In one study, the 1,5-AG levels of a patient group with HbA1c levels of 8.5 ± 1.6% and a control group with HbA1c levels of 5.1 ± 0.4% were reported as 4.0 ± 2.0 µg/mL and 24.7 ± 6.4 µg/mL, respectively [ 88 ]. Similarly, in a study conducted by Wang et al. [ 89 ] on 298 adults diagnosed with diabetes, serum 1,5-AG levels showed a statistically significant negative correlation with FPG, PPG, and HbA1c. While there is a growing body of literature examining the relationship between 1,5-AG and glycemic control parameters, research investigating the influence of lifestyle factors on 1,5-AG regulation remains limited. In the present study, glycemic control parameters and lifestyle variables potentially associated with 1,5-AG in the T2DM group were determined via multiple linear regression analysis (Table 5 ). The results of the multiple linear regression model indicated that the model explained 61.5% of the variance in 1,5-AG levels and was statistically significant (< 0.001). Each 1-unit increase in HbA1c was leaded to a decrease a 1.481-unit decrease in 1,5-AG levels (p < 0.001). This finding demonstrates that as glycemic control deteriorates, 1,5-AG levels decline, underscoring its potential utility as a biomarker for short-term glycemic regulation. It has been found that every 1-hour increase in sleep duration is associated with a 0.005-unit increase in 1.5-AG (p < 0.05) (Table 5 ). Insufficient sleep duration and quality may impair postprandial glycemic response by increasing insulin resistance, thereby indirectly reducing 1,5-AG levels. Thus, optimizing sleep duration may be considered an important lifestyle intervention in glycemic control strategies. Dietary energy density also emerged as a strong predictor of dietary quality. The significant negative association between DED and 1,5-AG (B =-1.704; p < 0.05) (Table 5 ) suggests that high–energy density diets may adversely affect glycemic control. Such diets, typically rich in refined carbohydrates, added sugars, and saturated fats, can exacerbate PPG fluctuations. Therefore, we think that 1,5-AG may be a metabolic marker sensitive to diet quality. The associations of PPG with DASS and MEQ scores were not statistically significant (p > 0.05) (Table 5 ). However, the direction of the beta coefficients may still provide clinically meaningful insights. The negative coefficient associated with the DASS score indicates that psychological stress may diminish 1,5-AG levels by exacerbating glycemic oscillations; however, this effect lacked significant statistical power in the study. Overall, these findings indicate that 1,5-AG is not only an alternative biochemical marker but also a sensitive indicator of glycemic control linked to lifestyle and dietary quality. In particular, HbA1c, sleep duration, and dietary energy density emerge as strong determinants that should be prioritized in clinical monitoring and individualized nutrition planning. Strengths of the Study This study examines the significant yet underexplored association between blood 1,5-AG, a short-term glycemic biomarker, and DED, alongside lifestyle factors in women recently diagnosed with T2DM. Examining 1,5-AG as an adjunct biomarker to HbA1c in the early diagnosis and management enhances the precision nutrition strategy in diabetes care. The study distinctly correlates biochemical, dietary, anthropometric, and lifestyle factors, providing a comprehensive metabolic profile of women with T2DM. It emphasizes critical factors influencing glycemic management, including sleep length and DED, indicating behavioral targets for therapy. Statistical analyses were suitable and encompassed normality tests, parametric and non-parametric comparisons, Pearson and Spearman correlations, and multiple linear regression. The paper addresses the rising global prevalence of diabetes and advocates for alternative, cost-effective, and responsive glycemic monitoring techniques, particularly pertinent in transitional countries such as Türkiye. Weaknesses and Limitations of the Study The study had 88 female participants, constraining the applicability of the findings to other genders and ethnically or socioeconomically varied groups. Despite the identification of connections, the cross-sectional design of the study precludes the determination of causality. As participants were recently diagnosed, they may have already undergone nutritional counseling, potentially influencing DED, macronutrient consumption, or meal habits. 5. Conclusion and Recommendations This study demonstrated that serum 1,5-AG concentrations are markedly reduced in women with newly diagnosed T2DM, underscoring its value as a short-term biomarker of glycemic regulation. Strong inverse associations with HbA1c, alongside correlations with DED and sleep duration, emphasize the influence of both metabolic and lifestyle factors on glycemic variability. Unlike HbA1c, which reflects long-term glycemia, 1,5-AG offers unique sensitivity to postprandial excursions and short-term fluctuations. The regression model explaining nearly 60% of the variance in 1,5-AG highlights its clinical potential. Incorporating 1,5-AG into routine assessments may improve individualized management, particularly for patients undergoing dietary or lifestyle interventions. Future longitudinal studies across more diverse populations are warranted to confirm its predictive role and to further elucidate links between 1,5-AG, nutrition, chronobiology, and metabolic health. In conclusion, this study revealed that serum 1,5-anhydroglucitol (1,5-AG) concentrations were markedly reduced in newly diagnosed women with type 2 diabetes mellitus (T2DM) relative to healthy controls, underscoring its potential as a sensitive biomarker for short-term glycemic management. The strong inverse relationship between 1,5-AG and HbA1c, together with its correlations with sleep duration and dietary energy density (DED), highlights the complex interplay of biological and behavioral factors affecting glycemic variability. In contrast to HbA1c, which indicates long-term average glycemia, 1,5-AG is especially useful for monitoring postprandial excursions and short-term variations. Abbreviations 1,5-AG: 1,5-Anhydroglucitol T2DM: Type 2 Diabetes Mellitus. DED: Dietary Energy Density HbA1c: Hemoglobin A1c BMI: Body Mass İndex FPG: Fasting Plasma Glucose PPG: Postprandial Glucose HOMA-IR: Homeostasis Model Assessment-Estimated İnsulin Resistance TC: Total Cholesterol TG: Triglycerides HDL-C: High-Density Lipoprotein Cholesterol LDL-C: Low-Density Lipoprotein Cholesterol WC: Waist Circumference PUFAs: Polyunsaturated Fatty Acids Declarations Ethics approval and consent to participate Ethical permission was secured by the Ethics Committee of Gazi University (Decision No: 24074710-29), and informed consent was acquired from all participants. Clear explanations were provided for the individuals with regard to the purpose of the study, after which written informed consent was obtained from all the individuals in accordance with the Declaration of Helsinki (World Medical Association). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Clinical trial number: Not applicable. Competing interests The authors declare that they have no competing interests. Funding The 1.5 AG analysis kits used in the study were covered by the Higher Education Council Faculty Member Training Program grant. Authors’ contributions T.K.; investigation, conceptualization, data curation, formal analysis, methodology, writing - review & editing. E.K; investigation, conceptualization, methodology, supervision, project administration. Y.M.A; supervision, review, and editing. All authors have read and agreed to the published version of the manuscript. Acknowledgements This research did not receive any specific support from public, commercial, or non-profit entities. I wish to convey my appreciation for the steadfast direction of my senior, Dr. Emine KOÇYİĞİT, in the support and execution of the study data. Authors’ information 1 Gümüşhane University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Gümüşhane, Turkey (T.K.) 2 Gazi University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Ankara, Turkey (E.K) 3 Gazi University, Faculty of Medicine, Department of Endocrinology, Ankara, Turkey (M.A) References Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, Ostolaza H, Martín C: Pathophysiology of Type 2 Diabetes Mellitus . Int J Mol Sci 2020, 21 (17). 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Juraschek SP, Steffes MW, Selvin E: Associations of alternative markers of glycemia with hemoglobin A1c and fasting glucose . Clinical chemistry 2012, 58 (12):1648-1655. Mehta SN, Schwartz N, Wood JR, Svoren BM, Laffel LM: Evaluation of 1, 5‐anhydroglucitol, hemoglobin A1c, and glucose levels in youth and young adults with type 1 diabetes and healthy controls . Pediatric diabetes 2012, 13 (3):278-284. Wang Y, Yuan Y, Zhang Y, Lei C, Zhou Y, He J, Sun Z: Serum 1, 5-anhydroglucitol level as a screening tool for diabetes mellitus in a community-based population at high risk of diabetes . Acta diabetologica 2017, 54 (5):425-431. Tables Tables 1 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1toTable5Associationbetweenserum15anhydroglucitol.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor invited by journal 11 Nov, 2025 Editor assigned by journal 07 Nov, 2025 Submission checks completed at journal 07 Nov, 2025 First submitted to journal 02 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8012414","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560835023,"identity":"5b62bb6a-2c5c-4285-89ea-553b7347f2f8","order_by":0,"name":"Tevfik KOÇAK","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYDACHhBxwAbMZoaKGeDXAdGShq4lgaCWwyRosec5fPDDjzPn7TZcO2P8uaDmjhwDe/M2CcYf93DbwtuWLNlz43byhts5ZtIzjj0zZuA5VibBkFCMWws/jxkDz4fbyQZALcw8bIcTGyRyzIBacLsMpIXxz4dzIC3Gn3n+Ha5vkH9DQAtvD9DwGwfsgFoMpHnbDicwSPAQ0HLmWLK0zJnkBMnbaWXSvH2HDdt40ootEtJwa2HvST748c0xO3u+28mbP/N8OyzPz354440PNri1wEBiA4zFBiIIawDGDxFqRsEoGAWjYKQCADWgUShfuWElAAAAAElFTkSuQmCC","orcid":"","institution":"Gümüşhane University","correspondingAuthor":true,"prefix":"","firstName":"Tevfik","middleName":"","lastName":"KOÇAK","suffix":""},{"id":560835026,"identity":"1c5ed0ef-9c09-4c25-9a41-0848b27194e9","order_by":1,"name":"Eda KÖKSAL","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Eda","middleName":"","lastName":"KÖKSAL","suffix":""},{"id":560835032,"identity":"f261ae3b-15ef-466e-80d8-6ed9a520180c","order_by":2,"name":"Mujde AKTURK","email":"","orcid":"","institution":"Gazi University","correspondingAuthor":false,"prefix":"","firstName":"Mujde","middleName":"","lastName":"AKTURK","suffix":""}],"badges":[],"createdAt":"2025-11-02 17:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8012414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8012414/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98747256,"identity":"6948788c-ca6c-46d3-8d68-5c53e08a0465","added_by":"auto","created_at":"2025-12-22 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08:51:03","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":290484,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8012414/v1/991ff37137f99f3cb49dbb3b.html"},{"id":98778988,"identity":"aa54bdc6-0a48-46ce-a27a-3be7be52bb4d","added_by":"auto","created_at":"2025-12-22 12:29:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":984200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe flow chart of the study Association between serum 1,5-anhydroglucitol \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ein here\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1TheflowchartofthestudyAssociationbetweenserum15anhydroglucitol.png","url":"https://assets-eu.researchsquare.com/files/rs-8012414/v1/1fa588d5a0c1f4ca8451f1bb.png"},{"id":98783345,"identity":"7fce34ed-1034-4918-9103-c2bbf0dc264a","added_by":"auto","created_at":"2025-12-22 12:41:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5434371,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8012414/v1/9f30e41c-12fa-43ca-bb95-4ef1f688a491.pdf"},{"id":98747260,"identity":"8b3f3673-4328-45af-89a9-5cb0a98f707d","added_by":"auto","created_at":"2025-12-22 08:51:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":88404,"visible":true,"origin":"","legend":"","description":"","filename":"Table1toTable5Associationbetweenserum15anhydroglucitol.docx","url":"https://assets-eu.researchsquare.com/files/rs-8012414/v1/014ba3441261f15b1d52fd0d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between serum 1,5-anhydroglucitol, dietary energy density and lifestyle factors in women with newly diagnosed type 2 diabetes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) is a chronic metabolic condition marked by sustained hyperglycemia due to decreased insulin production, insulin resistance, or a combination of both factors. The global incidence has been increasing significantly, primarily due to lifestyle-related variables like aging, gender disparities, poor dietary habits, obesity, physical inactivity, and changes in circadian rhythms resulting in sleep disorders. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Since 1990, the number of individuals living with T2DM has increased substantially, reaching approximately 828\u0026nbsp;million by 2022 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and this figure is projected to climb further, with the International Diabetes Federation (IDF) estimating 589\u0026nbsp;million adults with diabetes in 2024 and 853\u0026nbsp;million by 2050. This dramatic rise poses a major public health and economic challenge worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In T\u0026uuml;rkiye, the burden is equally alarming: as of 2021, nearly 9\u0026nbsp;million individuals had been diagnosed with T2DM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and the prevalence is expected to rise from 16.5% in 2024 to 18.6% by 2050, ranking the country among those with the highest diabetes prevalence in the European region.\u003c/p\u003e \u003cp\u003eNutrition is crucial in the development of T2DM and in attaining glycemic control [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In adults, dietary patterns characterized by high caloric density and shortened meal intervals are strongly associated with weight gain, obesity, and an elevated risk of T2DM [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Evidence further suggests that energy-dense diets reduce insulin sensitivity and increase glycemic variability [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Importantly, alterations in dietary composition and meal timing can modulate the expression and activity of enzymes involved in cholesterol, amino acid, lipid, glycogen, and glucose metabolism, thereby influencing overall energy homeostasis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Dietary alterations may lead to negative metabolic consequences, such as obesity, dyslipidemia, insulin resistance, and hypertension, ultimately disrupting postprandial glycemic control. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAttaining consistent long-term glycemic regulation is crucial to mitigating problems associated with T2DM and related chronic diseases. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Frequently utilized indicators encompass glycated hemoglobin (HbA1c), fasting glucose, and alternative biomarkers like 1,5-anhydroglucitol (1,5-AG), each representing glycemic state throughout varying temporal spans. [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although HbA1c reflects average glucose levels over the past 2\u0026ndash;3 months, it fails to sufficiently account for glycemic variability, including postprandial hyperglycemia or hypoglycemia over this timeframe. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, due to the gradual alteration of HbA1c levels post-therapeutic interventions, biomarkers that can swiftly and reliably indicate glycemic variations, exhibit metabolic stability, possess low biological variability, and are readily measurable provide significant benefits in diabetes management. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this context, serum 1,5-AG has emerged as a valuable short-term biomarker that addresses many of these limitations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e1,5-AG is a naturally occurring monosaccharide occurring monosaccharide in various foods foods, with its circulating levels maintained primarily through reabsorption in the renal proximal tubules [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Increased urine glucose excretion leads to competition between glucose and 1,5-AG for reabsorption, resulting in diminished tubular reuptake and decreased serum concentrations. Consequently, reduced serum 1,5-AG is acknowledged as a clinically significant indicator of postprandial hyperglycemia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Importantly, serum 1,5-AG reflects glycemic status over the preceding 1\u0026ndash;2 weeks, offering a short- to intermediate-term assessment that provides advantages over conventional markers in capturing postprandial glycemic excursions [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nonetheless, despite increasing data on its clinical relevance, there is insufficient research examining how dietary patterns and lifestyle factors affect blood 1,5-AG levels, highlighting a significant gap in comprehending its wider role in glycemic regulation.\u003c/p\u003e \u003cp\u003eNutrition and lifestyle are pivotal in the development and control of Type 2 Diabetes Mellitus (T2DM). Traditional indicators like HbA1c and fasting glucose are prevalent yet inadequate for identifying short-term glycemic fluctuation. Serum 1,5-AG has emerged as a viable alternative, providing a more sensitive indication of recent hyperglycemia variations. Nonetheless, its association with dietary energy density, nutritional status, and lifestyle variables is inadequately investigated. This study aims to investigate the relationships between serum 1,5-AG, conventional glycemic indicators, and specific dietary and lifestyle factors in persons with newly diagnosed T2DM.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis case–control study was conducted between September 2017 and April 2018 at Gazi University Hospital, Ankara, Türkiye. Participants in the case group were recruited from the Diabetes and Obesity Clinic within the Department of Endocrinology, whereas controls were enrolled through the Dietetics Clinic of the Department of Nutrition and Dietetics. The study population comprised 44 female patients newly diagnosed with T2DM and 44 age-matched healthy female volunteers, all between 45 and 65 years of age. Sample size was calculated a priori using power analysis, which indicated that a minimum of 40 participants per group was required to achieve 90% statistical power with a 5% margin of error.\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria for cases included a diabetes duration exceeding 51 years, a daily caloric intake below 600 kcal/d or above 4000 kcal/d, a diabetes duration surpassing two years, HbA1c levels outside the range of 6.5–8.5%, and the existence of chronic comorbidities such as renal, hepatic, parathyroid, thyroid, or cardiovascular diseases, hypertension, cancer, osteomalacia, chronic diarrhea, or malabsorption. Individuals with recorded sleep difficulties, mental illnesses, or microvascular and macrovascular consequences of type 2 diabetes mellitus were likewise eliminated. Furthermore, individuals who have undergone insulin therapy or pharmacological interventions, including oral antidiabetic agents excluding metformin, corticosteroids, vitamin-mineral supplements, osteoporosis medications, postmenopausal hormone therapy, or vitamin D supplementation in the preceding six months were deemed ineligible. Cases and controls were excluded if they indicated alcohol consumption, pregnancy, or lactation. The data for the study were collected using face-to-face interviews with a researcher-designed questionnaire encompassing sociodemographic and health information, anthropometric measures, body composition, biochemical data, and a 3-day dietary record.\u003c/p\u003e\n\u003cp\u003eThe study protocol was conducted according to the principles of the Declaration of Helsinki. Ethical permission was secured by the Ethics Committee of Gazi University Hospital (Decision No: 24074710-29), and informed consent was acquired from all participants. The flow chart of the study is presented in Fig. 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody weight, fat-free mass, body fat percentage, and body water percentage were assessed utilizing a portable body composition analyzer (Tanita BC 601). Height, waist circumference, and neck circumference were measured with an accuracy of 0.1 cm, while weight was measured with an accuracy of 0.1 kg [24]. The body mass index (BMI) was computed by dividing weight (kg) by height squared (m²) [25].\u003c/p\u003e\n\u003cp\u003eWaist circumference was measured with the individual standing, arms hanging at the sides, and legs together. The midpoint between the lowest rib and the cristae was marked using a non-stretchable tape measure, ensuring the tape measure was parallel to the floor without compressing the tissue. [26].\u003c/p\u003e\n\u003cp\u003eThe waist-to-height ratio and neck circumference were derived from waist and height measurements. These statistics are utilized to assess an individual's risk of chronic disease [27, 28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured using a Beckman Coulter analyzer. Insulin was analyzed via chemiluminescence (Beckman Coulter DxI). HbA1c was measured using the VARIANT II TURBO analyzer. C-peptide was measured using a Siemens IMMULITE 2000 analyzer. The reference range for 1,5-AG was 0–30.21 µg/mL. To assess insulin sensitivity, the \"homeostasis model assessment for insulin resistance\" (HOMA-IR) calculation was performed using the formula \"fasting blood glucose (mmol/L) x fasting insulin (µU/mL)/22.5.\" Values of \"≤2.7\" and \"\u0026gt;2.7\" were used for the HOMA-IR value [29].\u003c/p\u003e\n\u003ch3\u003e1,5-Anhydroglucitol Measurement\u003c/h3\u003e\n\u003cp\u003eBlood samples for 1,5-AG and glycated albumin were collected after 12 hours of fasting, centrifuged at 3000 rpm for 15 minutes, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Analyses were performed using the Mybiosource MBS012290 ELISA Kit in the Diagen Biotechnology Laboratory.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of Physical Activity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe nature and duration of activities undertaken by the individuals before the interview were documented on the activity record form utilizing a reminder technique. To determine the energy expended during physical activity, the documented durations of activities were multiplied by the energy costs (Physical Activity Ratio - PAR) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] based on the nature of the activity and the individuals' resting metabolic rate, as measured by a MedGem\u0026reg; Indirect calorimeter, to compute total energy expenditure (TEE). The durations derived from these activities (PAR values) were divided by 24 hours/1440 minutes to get the individual's daily Physical Activity Level (PAL) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eResting Metabolic Rate\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe resting metabolic rate (RMR) was assessed utilizing the MedGem\u0026reg; indirect calorimeter. Measurements were conducted during a minimum rest period of 15\u0026ndash;20 minutes in a tranquil, temperature-regulated environment. Each measurement endured for around 8 to 10 minutes. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of Circadian Rhythm\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Morningness-Eveningness Questionnaire (MEQ) was used to determine chronotype (morning, evening, or intermediate). The Turkish adaptation of the MEQ was validated by P\u0026uuml;nd\u0026uuml;k et al. in 2005 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Scores classified participants into five chronotypes: definite morning type (70\u0026ndash;86), moderately morning type (59\u0026ndash;69), intermediate type (42\u0026ndash;58), moderately evening type (31\u0026ndash;41), and definite evening type (16\u0026ndash;30). Lower scores have been linked to obesity and chronic disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of Stress\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Depression, Anxiety, and Stress Scale (DASS-21R) is a commonly utilized instrument for assessing levels of depression, anxiety, and stress. This scale has 21 items designed to assess the severity of psychological symptoms and offers insights into the levels of depression, anxiety, and stress across three primary dimensions. The DASS-21R is extensively utilized to evaluate the severity of various diseases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The DASS-21R questionnaire has significant usefulness in research and clinical environments, with the depression (α\u0026thinsp;=\u0026thinsp;0.83\u0026ndash;0.94), anxiety (α\u0026thinsp;=\u0026thinsp;0.66\u0026ndash;0.87), and stress (α\u0026thinsp;=\u0026thinsp;0.79\u0026ndash;0.91) subscales reflecting robust internal consistency [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The 21-item abbreviated Turkish version of the Depression, Anxiety, and Stress Scale has demonstrated validity and reliability [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssessment of Dietary Intake\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess and calculate the energy and nutrient intake of the individuals participating in the study, the researcher recorded three consecutive days of food consumption, two on weekdays and one on the weekend. At the beginning of the study, the researcher explained to each patient how to record food consumption, including examples, and provided a three-day record form for recording food intake along with meal times. The amounts of food or meals consumed, to be recorded on the food record form, were explained to the individual using the \"Food and Nutrition Photograph Catalog.\" [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Standardized recipes and portion sizes were derived from \u0026ldquo;Standard Meal Recipes\u0026rdquo;[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], \u0026ldquo;Selections from Traditional Turkish Cuisine\u0026rdquo;[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and \u0026ldquo;Examples from Turkish Cuisine\u0026rdquo;[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Energy and nutrient intakes were computed utilizing the Nutrition Information System (BeBis) version 7.2 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eCalculation of Dietary Energy Density and Intermeal time\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTwo different approaches were used to calculate dietary energy density, as used by Raynor et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] the first included all foods and all caloric beverages (solid foods\u0026thinsp;+\u0026thinsp;caloric beverages), and the second included all solid foods but excluded beverages (solid foods only). Fruit juices, alcoholic and non-alcoholic beverages, milk beverages, etc., were classified as beverages, while soups, yogurt, ice cream, and other similar products were classified as solid foods [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Density was calculated based on total daily food weight relative to total daily energy intake and recorded in kcal/gram. Dietary energy density was determined for three days, and then the average of the three days was calculated to determine each participant's average daily dietary energy density [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Intermeal intervals are the time difference between one main meal and the next. This time period can have a direct impact on an individual's metabolic status, nutritional habits, and energy needs.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the statistical evaluation of the data, the Statistical Package for the Social Sciences (SPSS, version: 22.0) statistical package program was used. Mean, standard deviation, median, minimum, and maximum values were used for continuous (quantitative) variables obtained through measurement, while frequency and percentage values were used for categorical (qualitative) variables. A normality test was performed before all data were analyzed. The data's compliance with normal distribution was examined using visual (histogram and probability plots) and analytical methods (Kolmogorov-Simirnov test). Student's t-test was applied to two-group data with normal distribution, and the Mann-Whitney U test, a non-parametric test, was applied to data without normal distribution. Homogeneity was assessed according to the Levene test statistic before applying the Student's t-test. In determining the correlation between numerical data, the Pearson correlation coefficient was used when both variables were normally distributed, and the Spearman correlation coefficient was used when at least one of the two variables was not normally distributed. The results were evaluated at a 95% confidence interval and a statistical significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eTable 1 summarizes the distribution of anthropometric characteristics, biochemical parameters, and lifestyle variables of the participants. The mean age of participants in the patient group was 53.16 ± 5.13 years, compared to 49.32 ± 3.73 years in the control group. The mean BMI values were 33.17 ± 6,16 kg/m² and 32.45 ± 5,09 kg/m² (p\u0026gt;0.05). While other biochemical indicators except TC, HDL-C and LDL-C; HbA1c, FPG, insulin, TG, HOMA-IR, C-peptide and 1.5-AG levels were significantly higher in the patient group than in the control group, 1.5-AG levels were significantly lower in the patient group than in the control group. Literature findings consistently demonstrate an inverse association of 1,5-AG with hyperglycemic regulation of HbA1c in diabetes, stronger associations with postprandial peaks and CGM variability measures compared to fasting blood glucose, and lower 1,5-AG in diabetes compared to controls. Therefore, our finding supports the value of 1,5-AG as a short-term marker of dysglycemia in patients with newly diagnosed type 2 diabetes. (p \u0026lt; 0.05). MEQ scores were significantly higher in the patient group compared to the control group (p = 0.050), and a greater tendency toward morning chronotype was detected among patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1. Distribution of general characteristics, biochemical, and lifestyle variables of participants\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ein here\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 delineates the distributions of individuals' daily calorie, nutrient, and DED consumption. Upon examination of the energy and macronutrient intakes of the patient and control groups, no statistically significant differences were seen in nutrient intake, excluding protein, carbohydrate, fat, polyunsaturated fatty acids (PUFAs), and fiber (p \u0026gt; 0.05). \u0026nbsp; The patient group had a markedly elevated intake of protein, carbohydrates, polyunsaturated fatty acids (PUFA), and fiber compared to the control group (p \u0026lt; 0.05). \u0026nbsp;Although no statistically significant difference was seen between the patient and control groups regarding the mean intermeal interval and DED values from liquid and solid foods (p \u0026gt; 0.05), the patient group exhibited a statistically significant reduction in DED from solid foods compared to the control group (p \u0026lt; 0.05). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2. Comparison of energy and nutrient intake between patient and control groups\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;in here\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 delineates the connections among anthropometric indicators, biochemical parameters, lifestyle variables, and glycemic control indicators in both the patient and control groups involved in the study. The BMI, waist circumference, waist-to-height ratio, and HDL-C levels of the patient group in the study exhibited a significant correlation with HOMA-IR levels (BMI r=0.450; waist circumference r=0.402; waist-to-height ratio r=0.321; HDL-C r=-0.363). Additionally, BMI, waist circumference, and HDL-C levels demonstrated a significant correlation with insulin levels (BMI r=0.407; waist circumference r=0.369; HDL-C r=-0.324) (p\u0026lt;0.05). \u0026nbsp;Significant relationships were observed between anthropometric indicators, lifestyle variables, and specifically, 1.5-AG demonstrated a significant negative association with anthropometric measurements of waist circumference and waist-to-height ratio (p\u0026lt;0.05). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3. Association of glycemic control markers with biochemical data, anthropometric measurements, and lifestyle factors of participants\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ein here\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 summarizes the distribution of the correlation of participants' nutrient intakes with glycemic control indicators. In the control group, longer intermeal time was positively correlated with 1,5-AG levels, indicating that longer intervals between meals may support better short-term glycemic control (r = 0,441, p = 0,003). No significant associations were observed in the patient group (p\u0026gt;0.05).\u0026nbsp;In the control group, energy intake correlated positively with insulin (r = 0,299, p = 0,048), HOMA-IR (r = 0,347, p = 0,021), and C-peptide (r = 0,336, p = 0,026), suggesting higher energy intake is linked to insulin resistance and β-cell activity. In the control group, carbohydrate (r = 0,305, p = 0,047) and protein intake (r = 0,300, p = 0,048) correlated positively with HbA1c, indicating a possible link between higher protein intake and poorer long-term glycemic control. In the patient group, positive correlations with insulin (r = 0,337, p = 0,025) and HOMA-IR (r = 0,361, p = 0,016), suggesting higher saturated fatty acids intake may worsen insulin resistance. In the control group, monounsaturated fatty acids were positively correlated with insulin r = 0,305, p = 0,047) and HOMA-IR (r = 0,300, p = 0,048). Fiber intake showed no significant associations with glycemic indicators in either group (p\u0026gt;0.05). Cholesterol intake correlated positively with C-peptide in the control group (r = 0,374, p = 0,048). In the control group, omega-3 intake correlated negatively with HbA1c (r = -0,313, p = 0,039)and positively with 1,5-AG (r = 0,300, p = 0,048), indicating a protective role against poor glycemic control (p\u0026lt;0.05). Omega-6 intake showed no significant associations (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4. Correlation of participants nutrient intakes with glycemic control indicators\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ein here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5. Multiple linear regression model for the relationship between 1,5 Anhidroglusitol and some variables of diabetic patients details the regression model. Multiple linear regression analysis was conducted to evaluate the relationship between 1,5-AG, HbA1c, FPG, sleep duration, DASS score, MEQ score, and DED in the patient group. The model was significant (R² = 0.615; p\u0026lt;0.001). The strong model fit (R² = 0.615) indicates that such a multidimensional framework is both clinically meaningful and methodologically robust, suggesting potential for 1.5-AG to be applied not only as a diagnostic adjunct but also as a lifestyle-sensitive marker for personalized nutrition interventions in diabetes management. Significant predictors of 1,5-AG levels were HbA1c (\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e:-1,481; p \u0026lt; 0.001), sleep duration (p\u0026lt;0.05), and DED (p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5. Multiple linear regression model for the relationship between 1,5 Anhidroglusitol and some variables of diabetic patient\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ein here\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe primary emphasis of our investigation was the association between blood 1,5-AG concentration and dietary habits, anthropometric measurements, diabetes-related biochemical markers, and lifestyle variables. The content of serum 1,5-AG exhibited a substantial connection with anthropometric factors, including BMI and waist circumference. Furthermore, the content of 1,5-AG exhibited a strong correlation with omega-3 consumption. Linear regression analysis demonstrated an inverse linear correlation between 1,5-AG levels and FPG and HbA1c concentrations in the diabetic cohort. The findings indicate that serum 1,5-AG levels diminish as FPG and HbA1c levels rise in individuals with diabetes. Furthermore, the identified correlations between 1,5-AG levels and DED and sleep, as nutritional and lifestyle determinants, may hold significance for future investigations. The study's strength is its demonstration of the utility of 1,5-AG as an alternative additional biomarker for evaluating short-term glycemic control in individuals with type 2 diabetes. The main limitation of our study is that the correlations of 1,5-AG levels were assessed only in female patients.\u003c/p\u003e \u003cp\u003eDietary behavior modification, physical activity, and lifestyle changes play an effective role in developing and preventing T2DM. Individualized dietary energy restriction, consumption of fiber-rich foods, careful planning of meal content and timing, adequate and balanced intake of whole grains, vitamins, and minerals, as well as limiting the intake of saturated fats and foods increasing T2DM risk, not only reduce the risk of developing T2DM but also play an active role in achieving glycemic control in obese patients with T2DM [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Sustained long-term tight glycemic control is critically important for preventing complications of T2DM and associated chronic conditions [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Among the main parameters for monitoring glycemic control, HbA1c, fasting, and postprandial plasma glucose values are considered most important; HbA1c, in particular, is closely related to complications and is used as the gold standard for assessing glycemic control [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Along with these parameters, the use of continuous glucose monitoring systems and 1,5-AG has been recommended to better monitor hypo- and hyperglycemic episodes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The advantage of 1,5-anhydroglucitol lies in its ability to reflect short-to-intermediate glycemic fluctuations and in its independence from hemoglobin metabolism, making it a promising new indicator for postprandial blood glucose levels [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. he 2011 International Diabetes Federation (IDF) guidelines reported that 1,5-AG could be a more effective indicator for glycemic control by more accurately reflecting short-term glycemic variability and postprandial hyperglycemia [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In our study, HbA1c, FPG, serum insulin, triglyceride, HOMA-IR, and C-peptide levels of the patient group were significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while 1,5-AG levels were significantly lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than those of the control group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings support the hypothesis that increased blood glucose levels lead to increased renal tubular excretion of 1,5-AG, resulting in decreased plasma levels. Interestingly, no significant differences were observed in BMI, waist circumference, or body fat percentage between groups, suggesting that biochemical dysregulation may precede or outweigh overt anthropometric differences in the early phase of T2DM. In addition, it is thought that the high levels of HbA1c, FPG, serum insulin levels, triglyceride levels, HOMA-IR, and C-peptide levels, which are due to the low compliance of patients with medical nutrition therapy and the lack of glycemic control of individuals, support the biochemical changes that occur after diabetes in the literature.\u003c/p\u003e \u003cp\u003eIn individuals with diabetes, an adequate and balanced dietary pattern, along with appropriate meal timing, is grounded in concepts derived from clinical research, portion control, and personalized lifestyle modifications [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Effective nutritional management is essential to comprehensive treatment for individuals with T2DM. Maintaining a regular number of meals, appropriate intervals between meals, and an optimal meal pattern plays a critical role in achieving glycemic control [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. To ensure postprandial glycemic control, it is crucial to select foods that promote the maintenance of normal blood glucose levels. Dietary carbohydrates are the primary macronutrients influencing postprandial glycemic responses. However, other macronutrients, including fat, protein, and dietary fiber, can also significantly affect two-hour postprandial glucose (PPG) levels [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. One study demonstrated that nutrient intake in the T2DM group\u0026mdash;specifically potassium, calcium, magnesium, zinc, iodine, carotene, vitamin D, tryptophan, and vitamin B\u003csub\u003e12\u003c/sub\u003e\u0026mdash;was inversely correlated with diabetes incidence, while carbohydrate and protein intake were significantly higher in this group [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Similarly, a prospective cross-sectional study conducted in Tunisia, which compared individuals with diabetes, prediabetes, and healthy controls, found that the diabetic group consumed hypercaloric diets high in carbohydrates and fats. In this group, elevated intake of vitamin A and sodium, along with reduced protein intake, was associated with impaired glucose homeostasis [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Irregular meal frequency and suboptimal intervals between meals can substantially increase the body\u0026rsquo;s allostatic load, leading to disturbances in glucose regulation and potentially contributing to the development of insulin resistance [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The concept of chrononutrition, which recognizes that meal timing\u0026mdash;alongside food quality and quantity\u0026mdash;plays a critical role in health, emphasizes the alignment of meals with circadian rhythms [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Frequent consumption of high\u0026ndash;dietary energy density (DED) meals, typically composed of refined grains, added sugars, trans fats, and high-fructose corn syrup, has been linked to increased prevalence of obesity and related diseases [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Furthermore, another study reported that liquid-form dietary patterns generally have a lower glycemic index (GI) and insulin index compared to solid-form diets, while solid foods lead to faster increases in blood glucose and insulin levels than their liquid counterparts [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Contrary to expectations, in our study, total energy intake did not differ significantly between patients and controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, dietary macronutrient distribution revealed notable distinctions: the T2DM group (2.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39 g/kcal) consumed higher proportions of protein and fat, particularly polyunsaturated fatty acids, and lower carbohydrate intake and DED from solid foods (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These dietary shifts may reflect conscious efforts toward carbohydrate restriction following diagnosis, in line with clinical recommendations for glycemic management [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The increased consumption of protein and fat highlights the intricacies of dietary modifications in diabetes and prompts inquiries about the long-term cardiometabolic consequences of macronutrient replacement. This finding may be explained by the fact that the participants in the T2DM group were newly diagnosed and had not yet fully adapted to the medical nutrition therapy prescribed after diagnosis, which\u0026mdash;combined with higher intakes of protein, carbohydrates, PUFAs, and fiber\u0026mdash;may have resulted in a lower DED from solid foods. Notably, fiber consumption did not differ significantly between groups; however, regression and correlation analyses indicated that increased dietary fiber may moderate glucose homeostasis, aligning with previous evidence connecting fiber to enhanced insulin sensitivity and PPG regulation [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The diminished DED noted in patients may signify healthier dietary selections; nonetheless, its correlation with decreased 1,5-AG levels implies that overall dietary quality and nutrient equilibrium are essential.\u003c/p\u003e \u003cp\u003eObesity is a major risk factor for T2DM, and body weight, height, and waist circumference (WC) can be easily assessed using cost-effective tools. However, BMI has certain limitations, as it does not differentiate between fat mass and lean body mass, which can lead to the misclassification of individuals with high muscle mass as overweight or obese. Although WC provides a more direct measure of central obesity, it is still insufficient for distinguishing between subcutaneous and visceral fat, which are associated with different metabolic risks [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In one study, reductions in BMI, body weight, and WC achieved through pharmacotherapy were significantly associated with improvements in glycemic control parameters [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Another study found that increased WC in women was associated with poorer HbA1c control and lipodystrophy [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Dyslipidemia frequently coexists with impaired glucose metabolism. In addition to conventional lipid parameters\u0026mdash;TG, TC, HDL-C, and LDL-C\u0026mdash;non-traditional lipid parameters such as TG/HDL-C, LDL-C/HDL-C, non-HDL-C, TC/HDL-C, and non-HDL-C/HDL-C are strongly associated with the onset and progression of prediabetes and type 2 diabetes [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Several studies have reported correlations between blood glucose levels and serum lipids or lipid ratios, with glucose levels positively associated with TG, LDL-C, and elevated TG/HDL-C and LDL-C/HDL-C ratios [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. In our study, insulin and HOMA-IR values\u0026mdash;two glycemic control parameters\u0026mdash;were significantly correlated with BMI, WC, waist-to-height ratio, and HDL-C levels in the T2DM group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the control group, nearly all glycemic control parameters founded significant correlations with anthropometric variables (BMI, WC, waist-to-height ratio) and lipid parameters (TC, HDL-C, and TG) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings support the importance of maintaining optimal cut-off values for anthropometric measures and lipid levels in both T2DM patients and healthy individuals to preserve glycemic regulation.\u003c/p\u003e \u003cp\u003eCircadian rhythm disruption has been linked to increased risks of metabolic disorders, T2DM, cardiovascular disease, obesity, and poor sleep quality [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. One study examining the interaction between circadian rhythm and chrononutrition demonstrated that altering meal timing reduced resting energy expenditure before meals, did not change postprandial energy expenditure, decreased fasting carbohydrate oxidation, impaired glucose tolerance, blunted the diurnal profile of free cortisol concentrations, and reduced the thermic effect of food [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Globally, poor sleep quality is a common problem among individuals with T2DM, affecting up to half of this population. Poor sleep quality can exacerbate complications by impairing glucose metabolism, increasing inflammation, altering hormonal regulation, and lowering quality of life. Therefore, improving sleep quality has become a key goal in diabetes management [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Studies have suggested that physical activity could be a primary focus in interventions aimed at improving sleep quality in T2DM populations [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Compared to non-diabetic individuals, patients with type 1 and type 2 diabetes have been reported to have higher resting metabolic rates (RMR), which may be attributed to increased catabolic rates, hyperglucagonemia, and enhanced gluconeogenic activity [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Evidence from previous research strongly supports the benefits of aerobic exercise in improving glycemic control among individuals with T2DM, emphasizing the importance of incorporating regular physical activity into diabetes management strategies to optimize health outcomes and reduce complication risks [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. In our study, no significant correlations were found between lifestyle variables (energy expenditure from physical activity, physical activity level, RMR, sleep duration, total stress score, and circadian rhythm score) and glycemic control indicators in the T2DM group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, in the control group, FPG was significantly correlated with circadian rhythm score, insulin and HOMA-IR were correlated with RMR, and C-peptide was correlated with physical activity variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings highlight the importance of lifestyle and anthropometric factors in glycemic regulation. Lifestyle characteristics, including physical activity, sleep duration, and circadian rhythm scores, exhibited minimal differences between groups; nonetheless, regression modeling indicated substantial correlations. The duration of sleep was favorably correlated with 1,5-AG, reinforcing the increasing acknowledgment of sleep as a factor influencing metabolic health.Interestingly, stress scores did not correlate with 1,5-AG, although psychological stress has been implicated in dysregulated glucose metabolism through neuroendocrine pathways [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. The lack of significance in our study may be due to sample size or the cross-sectional design, limiting temporal inference.\u003c/p\u003e \u003cp\u003eA systematic review of 102 randomized controlled trials investigating the effects of altering dietary fat and carbohydrate composition on blood glucose control, insulin sensitivity, and insulin secretion found that replacing dietary carbohydrates with saturated fat did not significantly affect glycemic control indicators. However, replacing carbohydrates and saturated fat with unsaturated fats\u0026mdash;particularly PUFAs\u0026mdash;was found to benefit blood glucose control, insulin sensitivity, and insulin secretion [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. In a study by Fujii et al. [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] examining the relationship between fatty acids and insulin resistance in Japanese individuals, the percentage of linoleic acid intake was negatively correlated with visceral adipose tissue, fasting glucose, HbA1c, HOMA-IR, and systolic blood pressure. Similarly, Wanders et al. [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] reported a positive association between saturated fatty acid intake (compared to carbohydrates) and fasting insulin and HOMA-IR levels. Additionally, total fatty acid intake from meat and meat products was positively associated with fasting insulin levels. In another study by Nigam et al. [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] investigating plasma n-3, n-6, and PUFA levels and insulin resistance in individuals with metabolic syndrome, HOMA-IR was positively correlated with total saturated fatty acid and n-6 fatty acid levels, while negatively correlated with total n-3 fatty acid levels. Moreover, total n-3 and n-6 fatty acid levels and the n-6/n-3 ratio were found to be associated with HOMA-IR levels. Consistent with these findings, our study also revealed a positive and significant correlation between dietary saturated fat intake percentage and HOMA-IR values in the T2DM group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the control group, significant positive correlations were found between omega-3 intake and 1,5-anhydroglucitol, cholesterol intake and FPG, protein, carbohydrate, and trans fatty acid intake and HOMA-IR, as well as trans fatty acid intake and insulin levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, in our study, a positive and significant correlation was found between saturated fat intake and HOMA-IR values ​​in the diabetic group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the control group, a positive and significant correlation was found between dietary omega-3 intake and 1.5 AG, cholesterol intake and FPG, protein, carbohydrate, and monounsaturated fatty acid intake and HOMA-IR, and TSFA intake and insulin levels. (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05) (Tablo 4). This indicates that macronutrient consumption is directly or indirectly associated with markers indicative of glycemic control. Diabetes, particularly when complicated by obesity, represents a major health concern in modern society. Dietary planning should therefore consider individual characteristics such as age, sex, and BMI, tailoring macronutrient and micronutrient intake to the individual. Experimental studies have shown that high\u0026ndash;dietary energy density meals increase PPG levels, free fatty acid concentrations, and insulin resistance [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. However, in our study, no statistically significant relationship was found between biochemical parameters and dietary energy density or meal interval duration in the T2DM group (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the control group, HbA1c levels were positively correlated with meal interval duration (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding may be explained by the higher DED in the control group compared to the T2DM group, where increased DED could induce glycemic fluctuations, thus leading to a positive correlation with HbA1c.\u003c/p\u003e \u003cp\u003eInterest in 1,5-anhydroglucitol (1,5-AG) as an alternative to blood glucose or HbA1c is increasing. A short-term glycemic marker sensitive to glycemic fluctuations may provide valuable and timely feedback in diabetic patients following therapeutic and dietary interventions, even before changes in HbA1c become apparent [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. In one study, the 1,5-AG levels of a patient group with HbA1c levels of 8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6% and a control group with HbA1c levels of 5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4% were reported as 4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 \u0026micro;g/mL and 24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 \u0026micro;g/mL, respectively [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Similarly, in a study conducted by Wang et al. [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] on 298 adults diagnosed with diabetes, serum 1,5-AG levels showed a statistically significant negative correlation with FPG, PPG, and HbA1c. While there is a growing body of literature examining the relationship between 1,5-AG and glycemic control parameters, research investigating the influence of lifestyle factors on 1,5-AG regulation remains limited. In the present study, glycemic control parameters and lifestyle variables potentially associated with 1,5-AG in the T2DM group were determined via multiple linear regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results of the multiple linear regression model indicated that the model explained 61.5% of the variance in 1,5-AG levels and was statistically significant (\u0026lt;\u0026thinsp;0.001). Each 1-unit increase in HbA1c was leaded to a decrease a 1.481-unit decrease in 1,5-AG levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This finding demonstrates that as glycemic control deteriorates, 1,5-AG levels decline, underscoring its potential utility as a biomarker for short-term glycemic regulation. It has been found that every 1-hour increase in sleep duration is associated with a 0.005-unit increase in 1.5-AG (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsufficient sleep duration and quality may impair postprandial glycemic response by increasing insulin resistance, thereby indirectly reducing 1,5-AG levels. Thus, optimizing sleep duration may be considered an important lifestyle intervention in glycemic control strategies. Dietary energy density also emerged as a strong predictor of dietary quality. The significant negative association between DED and 1,5-AG (B =-1.704; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) suggests that high\u0026ndash;energy density diets may adversely affect glycemic control. Such diets, typically rich in refined carbohydrates, added sugars, and saturated fats, can exacerbate PPG fluctuations. Therefore, we think that 1,5-AG may be a metabolic marker sensitive to diet quality. The associations of PPG with DASS and MEQ scores were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, the direction of the beta coefficients may still provide clinically meaningful insights. The negative coefficient associated with the DASS score indicates that psychological stress may diminish 1,5-AG levels by exacerbating glycemic oscillations; however, this effect lacked significant statistical power in the study. Overall, these findings indicate that 1,5-AG is not only an alternative biochemical marker but also a sensitive indicator of glycemic control linked to lifestyle and dietary quality. In particular, HbA1c, sleep duration, and dietary energy density emerge as strong determinants that should be prioritized in clinical monitoring and individualized nutrition planning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrengths of the Study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study examines the significant yet underexplored association between blood 1,5-AG, a short-term glycemic biomarker, and DED, alongside lifestyle factors in women recently diagnosed with T2DM.\u003c/p\u003e \u003cp\u003eExamining 1,5-AG as an adjunct biomarker to HbA1c in the early diagnosis and management enhances the precision nutrition strategy in diabetes care.\u003c/p\u003e \u003cp\u003eThe study distinctly correlates biochemical, dietary, anthropometric, and lifestyle factors, providing a comprehensive metabolic profile of women with T2DM.\u003c/p\u003e \u003cp\u003eIt emphasizes critical factors influencing glycemic management, including sleep length and DED, indicating behavioral targets for therapy.\u003c/p\u003e \u003cp\u003eStatistical analyses were suitable and encompassed normality tests, parametric and non-parametric comparisons, Pearson and Spearman correlations, and multiple linear regression.\u003c/p\u003e \u003cp\u003eThe paper addresses the rising global prevalence of diabetes and advocates for alternative, cost-effective, and responsive glycemic monitoring techniques, particularly pertinent in transitional countries such as T\u0026uuml;rkiye.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWeaknesses and Limitations of the Study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study had 88 female participants, constraining the applicability of the findings to other genders and ethnically or socioeconomically varied groups.\u003c/p\u003e \u003cp\u003eDespite the identification of connections, the cross-sectional design of the study precludes the determination of causality.\u003c/p\u003e \u003cp\u003eAs participants were recently diagnosed, they may have already undergone nutritional counseling, potentially influencing DED, macronutrient consumption, or meal habits.\u003c/p\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cp\u003eThis study demonstrated that serum 1,5-AG concentrations are markedly reduced in women with newly diagnosed T2DM, underscoring its value as a short-term biomarker of glycemic regulation. Strong inverse associations with HbA1c, alongside correlations with DED and sleep duration, emphasize the influence of both metabolic and lifestyle factors on glycemic variability. Unlike HbA1c, which reflects long-term glycemia, 1,5-AG offers unique sensitivity to postprandial excursions and short-term fluctuations.\u003c/p\u003e \u003cp\u003eThe regression model explaining nearly 60% of the variance in 1,5-AG highlights its clinical potential. Incorporating 1,5-AG into routine assessments may improve individualized management, particularly for patients undergoing dietary or lifestyle interventions. Future longitudinal studies across more diverse populations are warranted to confirm its predictive role and to further elucidate links between 1,5-AG, nutrition, chronobiology, and metabolic health.\u003c/p\u003e \u003cp\u003eIn conclusion, this study revealed that serum 1,5-anhydroglucitol (1,5-AG) concentrations were markedly reduced in newly diagnosed women with type 2 diabetes mellitus (T2DM) relative to healthy controls, underscoring its potential as a sensitive biomarker for short-term glycemic management. The strong inverse relationship between 1,5-AG and HbA1c, together with its correlations with sleep duration and dietary energy density (DED), highlights the complex interplay of biological and behavioral factors affecting glycemic variability. In contrast to HbA1c, which indicates long-term average glycemia, 1,5-AG is especially useful for monitoring postprandial excursions and short-term variations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e1,5-AG: 1,5-Anhydroglucitol\u003c/p\u003e\n\u003cp\u003eT2DM: Type 2 Diabetes Mellitus. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDED: Dietary Energy Density\u003c/p\u003e\n\u003cp\u003eHbA1c: Hemoglobin A1c\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass İndex\u003c/p\u003e\n\u003cp\u003eFPG: Fasting Plasma Glucose\u003c/p\u003e\n\u003cp\u003ePPG: Postprandial Glucose\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHOMA-IR: \u0026nbsp;Homeostasis Model Assessment-Estimated İnsulin Resistance\u003c/p\u003e\n\u003cp\u003eTC: Total Cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTG: Triglycerides \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHDL-C: \u0026nbsp; High-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C: Low-Density Lipoprotein Cholesterol\u003c/p\u003e\n\u003cp\u003eWC: Waist Circumference\u003c/p\u003e\n\u003cp\u003ePUFAs: Polyunsaturated Fatty Acids\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical permission was secured by the Ethics Committee of Gazi University (Decision No: 24074710-29), and informed consent was acquired from all participants. Clear explanations were provided for the individuals with regard to the purpose of the study, after which written informed consent was obtained from all the individuals in accordance with the Declaration of Helsinki (World Medical Association).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 1.5 AG analysis kits used in the study were covered by the Higher Education Council Faculty Member Training Program grant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.K.; investigation, conceptualization, data curation, formal analysis, methodology, writing - review \u0026amp; editing. E.K; investigation, conceptualization, methodology, supervision, project administration. Y.M.A; supervision, review, and editing. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific support from public, commercial, or non-profit entities. I wish to convey my appreciation for the steadfast direction of my senior, Dr. Emine KOÇYİĞİT, in the support and execution of the study data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Gümüşhane University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Gümüşhane, Turkey (T.K.) \u003csup\u003e2\u003c/sup\u003e Gazi University, Faculty of Health Sciences, Department of Nutrition and Dietetics, Ankara, Turkey (E.K) \u003csup\u003e3\u003c/sup\u003e Gazi University, Faculty of Medicine, Department of Endocrinology, Ankara, Turkey (M.A)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGalicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, Ostolaza H, Mart\u0026iacute;n C: \u003cstrong\u003ePathophysiology of Type 2 Diabetes Mellitus\u003c/strong\u003e. \u003cem\u003eInt J Mol Sci \u003c/em\u003e2020, \u003cstrong\u003e21\u003c/strong\u003e(17).\u003c/li\u003e\n\u003cli\u003eZhou B, Rayner AW, Gregg EW, Sheffer KE, Carrillo-Larco RM, Bennett JE, Shaw JE, Paciorek CJ, Singleton RK, Barradas Pires A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eWorldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants\u003c/strong\u003e. \u003cem\u003eThe Lancet \u003c/em\u003e2024, \u003cstrong\u003e404\u003c/strong\u003e(10467):2077-2093.\u003c/li\u003e\n\u003cli\u003eCeriello A, Colagiuri S: \u003cstrong\u003eIDF global clinical practice recommendations for managing type 2 diabetes\u0026ndash;2025\u003c/strong\u003e. \u003cem\u003eDiabetes Research and Clinical Practice \u003c/em\u003e2025:112152.\u003c/li\u003e\n\u003cli\u003eBakanlığı TS, Kurumu HS: \u003cstrong\u003eT\u0026uuml;rkiye Diyabet Programı 2023-2027\u003c/strong\u003e. 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fast-food\u0026ndash;style breakfast results in an increase in oxidative stress in metabolic syndrome\u003c/strong\u003e. \u003cem\u003eMetabolism \u003c/em\u003e2008, \u003cstrong\u003e57\u003c/strong\u003e(6):867-870.\u003c/li\u003e\n\u003cli\u003eVernarelli JA, Mitchell DC, Rolls BJ, Hartman TJ: \u003cstrong\u003eDietary energy density is associated with obesity and other biomarkers of chronic disease in US adults\u003c/strong\u003e. \u003cem\u003eEuropean journal of nutrition \u003c/em\u003e2015, \u003cstrong\u003e54\u003c/strong\u003e(1):59-65.\u003c/li\u003e\n\u003cli\u003eJuraschek SP, Steffes MW, Selvin E: \u003cstrong\u003eAssociations of alternative markers of glycemia with hemoglobin A1c and fasting glucose\u003c/strong\u003e. \u003cem\u003eClinical chemistry \u003c/em\u003e2012, \u003cstrong\u003e58\u003c/strong\u003e(12):1648-1655.\u003c/li\u003e\n\u003cli\u003eMehta SN, Schwartz N, Wood JR, Svoren BM, Laffel LM: \u003cstrong\u003eEvaluation of 1, 5‐anhydroglucitol, hemoglobin A1c, and glucose levels in youth and young adults with type 1 diabetes and healthy controls\u003c/strong\u003e. \u003cem\u003ePediatric diabetes \u003c/em\u003e2012, \u003cstrong\u003e13\u003c/strong\u003e(3):278-284.\u003c/li\u003e\n\u003cli\u003eWang Y, Yuan Y, Zhang Y, Lei C, Zhou Y, He J, Sun Z: \u003cstrong\u003eSerum 1, 5-anhydroglucitol level as a screening tool for diabetes mellitus in a community-based population at high risk of diabetes\u003c/strong\u003e. \u003cem\u003eActa diabetologica \u003c/em\u003e2017, \u003cstrong\u003e54\u003c/strong\u003e(5):425-431.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, 1,5-anhydroglucitol, glycemic control, alternative biomarker","lastPublishedDoi":"10.21203/rs.3.rs-8012414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8012414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eHigh dietary energy density (DED) is a recognized risk factor for obesity and type 2 diabetes mellitus (T2DM), whereas balanced diets and adequate physical activity support glycemic regulation. This study investigated serum 1,5-anhydroglucitol (1,5-AG) as a short-term biomarker of glycemic control in women with newly diagnosed T2DM and explored its associations with glycemic markers, DED, and lifestyle factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEighty-eight women (44 with T2DM, 44 healthy controls; 45\u0026ndash;65 years) participated. Sociodemographic data, medical history, and physical activity were recorded. Anthropometric and body composition measures were obtained, and biochemical parameters (HbA1c, fasting glucose, insulin, cholesterol fractions, triglycerides, C-peptide, and 1,5-AG) were analyzed. Dietary intake was assessed using 3-day dietary records, and DED was calculated as energy (kcal) from foods, excluding beverages, per gram.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSerum 1,5-AG was significantly lower in T2DM patients (5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.42 \u0026micro;g/mL) compared with controls (13.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96 \u0026micro;g/mL) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). HbA1c, fasting glucose, insulin, C-peptide, triglycerides, and HOMA-IR were significantly elevated in T2DM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although total energy intake was similar, patients consumed more protein, fat, polyunsaturated fatty acids, and fiber, while carbohydrate intake and DED were lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlations emerged between dietary/lifestyle factors and glycemic indicators. In multivariable regression, 1,5-AG was independently associated with HbA1c (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sleep duration (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and DED (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eReduced serum 1,5-AG in T2DM supports its utility as a short-term biomarker of glycemic regulation. Its links with HbA1c, sleep, and DED emphasize the interplay between lifestyle and metabolic control. Integrating 1,5-AG into clinical evaluation may enhance individualized management in T2DM.\u003c/p\u003e","manuscriptTitle":"Association between serum 1,5-anhydroglucitol, dietary energy density and lifestyle factors in women with newly diagnosed type 2 diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:50:58","doi":"10.21203/rs.3.rs-8012414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-25T20:54:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161729975903258444323146245019194160852","date":"2025-12-12T03:33:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-11T17:52:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-11T08:44:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-07T06:46:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T06:46:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-11-02T17:05:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69cb5715-934b-4735-8240-36ba1f1bd842","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T08:50:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:50:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8012414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8012414","identity":"rs-8012414","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}