Dietary intake patterns and nutritional adequacy among adults with overweight or obesity treated with GLP-1 or dual GIP/GLP-1 receptor agonists

Dietary intake patterns and nutritional adequacy among adults with overweight or obesity treated with GLP-1 or dual GIP/GLP-1 receptor agonists | 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 Dietary intake patterns and nutritional adequacy among adults with overweight or obesity treated with GLP-1 or dual GIP/GLP-1 receptor agonists Ewa stachowska Stachowska, Sebastian Korus, Danuta Cembrowska-Lech, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7640335/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Background: GLP-1 dual GIP/GLP-1 agonists significantly suppress appetite, but it is unclear whether the typical diet of patients treated with these drugs still meets their basic nutritional needs. Objective: To assess nutrients intake among adults undergoing pharmacotherapy and identify dietary predictors of weight loss efficacy. Methods: This retrospective cohort study was conducted online among social media support groups (April 2024–February 2025). Participants included 387 adults, who reported regular once-weekly use of a GLP-1 agonist or dual GIP/GLP-1 agonist and completed 48-hour food diaries (one weekday and one weekend day). Daily energy and nutrient intake were reconstructed using a specialized software (Diet 6) containing an up-to-date database and standards issued by the National Institute of Public Health (NIZP PZH – PIB 2024). Differences between week and weekends were analyzed using a paired-samples t-test. Multiple linear regression assessed dietary and treatment predictors of weight loss. Results: Average energy intake was 753 kcal (SD 257.8 kcal), with protein 33.4 g (SD 15.3 g), fat 26.5 g (SD 12.4 g), carbohydrates 96.4 g (SD 35.6 g), and fiber 7.2 g (SD 3.1 g). Less than 10 % of participants met the recommended intakes for protein. Weekday intake was significantly higher by 170 kcal (95% CI 152–185 kcal; p < 0.001), with greater consumption of fat (8–9 g), and sodium (370 mg). Higher total protein intake promoted weight loss (β = 0.446; p = 0.014), while a higher proportion of animal protein (β = –0.517; p = 0.004) and higher sodium intake (β ≈ –0.002; p = 0.013) reduced it. Conclusions Adults receiving GLP-1 pharmacotherapy exhibit insufficient protein and critical micronutrients, alongside excess fat and sodium intake on weekdays. Routine dietary education emphasizing protein adequacy and micronutrient sufficiency should accompany incretin therapy to prevent nutritional deficiencies, sarcopenia, and optimize weight loss outcomes. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction GLP-1 receptor agonists and dual GIP/GLP-1 agonists are currently the mainstay of pharmacotherapy for obesity and type 2 diabetes. 1 – 5 By delaying gastric emptying and increasing satiety, these drugs enable significant weight loss and improve cardiovascular and metabolic risk 6 – 9 . By influencing the hunger and satiety centers in the hypothalamus and the reward system, these drugs enable significant weight loss and improve cardiovascular and metabolic risk also through their direct effects. 10 – 12 1 However, the same mechanisms that promote energy deficit can lead to chronic protein and micronutrient deficiencies, increasing the risk of sarcopenia, impaired wound healing, and reduced immunity 13 – 15 . It is estimated that 9–12 million people worldwide are currently using GLP-1-based therapy, but their usual diet has not yet been systematically assessed. 16 Previous studies have focused mainly on weight loss and glycemic control; few describe the macronutrient and micronutrient profile during therapy, and none compare intake on weekdays and weekends 17 – 19 . There is also a lack of data on whether treatment duration, drug class (GLP-1 vs. GIP/GLP-1), or qualitative diet composition modify weight loss efficacy. 3 , 5 , 20 Rapid loss of lean body mass is an independent risk factor for sarcopenia, cholelithiasis, recurrence of metabolic disorders, and weight regain after drug discontinuation. 21 – 25 The aim of this study was to assess energy intake and macro- and micronutrient intake on weekdays and weekends in adults reporting regular use of GLP-1 or GIP/GLP-1 agonists. The hypotheses were that (1) most patients do not meet the recommended intake standards for protein and key micronutrients, and (2) higher total protein intake, but not a predominance of animal protein, independently predicts greater weight loss, after adjusting for drug class and duration of therapy. Methods Study design The study was designed as a retrospective observational cohort conducted exclusively online, in accordance with the STROBE guidelines for observational epidemiological studies. The analysis includes users of GLP-1 receptor agonists (dulaglutide, liraglutide, semaglutide) or dual GIP/GLP-1 agonists (tirzepatide) recruited from Polish online communities. The project was approved by the Bioethics Committee of the Pomeranian Medical University, which determined that an anonymous survey did not require full review. All participants provided their informed consent electronically. Data source The data was collected using a Google Forms form hosted on a secure researcher account with two-factor authentication. The anonymized data sets were stored and analyzed exclusively locally; the identifiers corresponded to random codes assigned to the respondents, which complies with the requirements of the GDPR. Participants were recruited between April 2024 and February 2025. Recruitment channels included targeted posts in Polish-language support groups on Facebook™ and Instagram™ for people with obesity and/or diabetes using incretins. The inclusion criteria for the study were: age ≥ 18 years, current use of GLP-1 or GLP-1 and GIP analogues, pharmacological treatment for obesity or metabolic disorders, and informed consent to participate in the study. Minors, individuals not taking the listed medications, or those who did not complete the questionnaire were excluded from participation. Anthropometric data (body weight, height) were collected before the start of therapy and at the time of the study, along with a complete 48-hour weighted food diary (typical weekday and weekend). The final sample included 387 adults (80.4% women; mean age 34.9 ± 9.9 years; baseline BMI 36.4 ± 5.0 kg/m²). Types of drugs used by respondents: GLP-1 RA (dulaglutide, liraglutide, semaglutide) vs. dual GIP/GLP-1 agonist (tirzepatide). Average duration of therapy reported by respondents was 15.7 (SD 14 weeks). The study collected information on dietary profiles. The form consisted of two parts covering demographic data – age, gender, body weight before therapy and current body weight, height, duration of drug use (weeks), type of preparation, and any bariatric surgery. A 48-hour food diary – weighted, covering one typical working day and one weekend day; respondents recorded the time, weight or household measure, method of preparation, and brand of the product. Before sending the form, the system checked for completeness, and the user could correct any omissions. Respondents recorded in detail all food items with weighed portions or household measures for two days: a working day and a weekend day. The records were transferred to the Dieta-6 software (Poland, NIZP PHZ-PIB 2024 database), which generated values for energy and 42 nutrients. The results were compared with current Polish nutritional standards. Data were obtained on the average daily intake of macronutrients and key micronutrients (total protein, animal/plant protein, fiber, calcium, potassium, vitamins D, E, C) on weekdays and weekends. The primary endpoints in this study were to estimate the percentage of participants meeting the RDA/AI standards for protein and selected micronutrients, and the difference in energy and macronutrient intake between weekdays and weekends. Secondary endpoints were the reported change in body weight (kg) and BMI from the start of treatment. The study complies with the principles of the Declaration of Helsinki. Anonymization prevents the identification of individuals, therefore, in analogy to the JAMA study, no additional consent was required for the sharing of secondary data. The results of diet adequacy and body weight/BMI changes were analyzed separately. Study population Outcomes Adequacy of intake included consumption of carbohydrates, total fat, fiber, protein (total and divided into animal/plant sources) and sodium from Day 1, Day 2, and their average was compared with the Polish Nutrition Standards 2024 (DRI). For each of the five food categories, each observation was classified as deficit – value upper limit of DRI. From these classifications, the percentage of participants in each category was calculated for Day 1, Day 2 and the two-day average; these results were visualized to show the degree of compliance with national recommendations. The primary outcome was the proportion of participants in the “balanced” category for each component at the three measurement points. Secondary outcomes included (a) the difference in energy, macronutrient, and fiber intake between weekdays and weekends; self-reported change in body weight (kg) and BMI from the start of incretin therapy, calculated using the formula % difference = 100 × ((day 2 − day 1) / day 1). The distributions of key variables were assessed visually (histograms, box plots) and based on skewness; variables showing significant asymmetry were log-transformed or standardized. For paired comparisons (Day 1 vs. Day 2), a t-test for dependent samples was used. The relationship between body weight/BMI changes and dietary factors, age, and gender was assessed using an OLS regression model; β coefficients, 95% CI, and p-values were reported; p-value of < 0.05 was considered statistically significant. Table 1 Study population baseline characteristics. Characteristics Mean or N (SD or %) (N = 387) Sex, F / M 311 (80.4) / 76 (19.6) Age, year 34.9 (9.9) Baseline weight, kg 102.4 (16.9) Baseline BMI 36.4 (5.0) Baseline BMI class • Overweight: 27–30 21 (5.43) • Obesity class 1 (30 to < 35) 153 (39.53) • Obesity class 2 (35 to < 40) 139 (35.92) • Obesity class 3 (≥ 40) 74 (19.12) Index medication 387 (100) • Dulaglutide 2 (0.5) • Liraglutide 88 (22.7) • Semaglutide 211 (54.5) • Tirzepatide 86 (22.3) Bariatric surgery history 7 (1.8) Values are unadjusted means (SD) or n (%). Abbreviations: F, female; M, male; BMI, body mass index. Statistical analysis All statistical analyses were performed to evaluate changes between day 1 and day 2 and to examine how dietary intake related to observed differences in body weight and BMI. For each participant, absolute and percentage changes were computed, with percentage change defined as 100 × ((day 2 – day 1)/day 1). Data distributions for body weight, macronutrient intake, and their day-to-day changes were assessed visually and with the Shapiro–Wilk test. Normally distributed variables were compared using paired t-tests, whereas non-normally distributed variables were evaluated with the Wilcoxon signed-rank test. Associations between weight or BMI changes and dietary intake were further explored using ordinary least squares regression. Models included absolute changes in macronutrients (protein, fat, carbohydrate, fiber, and sodium), age, sex, treatment time, and receptor agonist use as covariates. In sensitivity analyses, macronutrient intake was expressed as percentage of total energy. Regression coefficients (β) with 95% CIs are reported. To contextualize dietary patterns, intakes of macronutrients and sodium were compared against the Polish Dietary Reference Intakes and classified as below, within, or above the recommended ranges. The proportions of participants in each category were calculated for day 1, day 2, and the average of both days. All analyses were conducted using Python (v3.10; statsmodels package). Statistical significance was set at P < 0.05 (2-sided). Results Dietary intake patterns compared with Polish Dietary Reference Intakes Figure 1 shows the distribution of carbohydrate, fat, protein, fiber, and sodium intakes relative to Dietary Reference Intakes across two 24-h dietary records. Analysis of two-day dietary records showed that approximately half of participants consumed carbohydrates within the recommended range on both days (51–53%) and when averaged across days (59%). About one-third consumed less than recommended, while 6–10% exceeded the upper limit. Fat intake varied between days. On day 1, participants were evenly distributed across categories of below (37%), within (34%), and above (29%) the Adequate Intake (AI). By day 2, nearly two-thirds consumed less than the AI, <30% met the guideline, and ~7% exceeded it. Based on the two-day average, 38% met the recommendation, 49% consumed less, and 13% exceeded it. Fiber intake was markedly inadequate. The mean intake was 7.2 g/day, and no participant met the recommendation of ≥25 g/day. Protein intake was also substantially below the Recommended Dietary Allowance (RDA). Across both days, ~97% failed to meet the requirement, while only 2–4% exceeded it. None met the exact RDA. Sodium intake was more aligned with recommendations: 72–85% of participants were at or near the AI, while 15–28% exceeded it; no participant consumed less than the guideline. To sum up, this profile points to a carbohydrate-adequate but protein- and fiber-poor diet with unstable fat intake, highlighting protein and fiber (and, secondarily, fat) as primary targets for dietary improvement. Within-week day-to-day variability in macronutrient intake Paired analyses compared nutrient intake between day 1 and day 2 of the final intervention week. Across the two consecutive recall days in the final week, most macronutrient intakes were stable ( Figure 2 ). For protein, total intake did not differ between days (mean difference = 0.02 g; 95% CI: –0.88, 1.68; P = 0.54). Similarly, no differences were observed for plant-based protein (0.02 g; 95% CI: –0.48, 0.64; P = 0.78). By contrast, animal-based protein intake was significantly higher on day 1 than on day 2 (0.56 g; 95% CI: 7.34, 9.16; P < 0.001). Fat intake also differed between days, with participants consuming 7–10 g more on day 1 than on day 2 (0.60 g; 95% CI: 7.25, 9.64; P < 0.001). In contrast, carbohydrate and fiber intakes were stable across days, with no significant mean differences [carbohydrates: 0.02 g; 95% CI: –4.83, 3.23; P = 0.71; fiber: 0.01 g; 95% CI: –0.37, 0.36; P = 0.98]. Sodium intake was substantially higher on day 1, averaging ~370 mg more than on day 2 (0.56 mg; 95% CI: 316.76, 435.93; P < 0.001). Taken together, these results indicate that the day-to-day variation in protein intake reflected shifts in protein sources rather than total protein, and that higher day-1 sodium accompanied higher fat intake. Day-to-day differences in energy intake and macronutrient distribution As shown in Figure 3 , participants consumed significantly more total energy on day 1 compared with day 2, whereas the proportion of energy from protein, carbohydrate, and fiber did not differ between days. Paired analyses showed that total energy intake was significantly higher on day 1 compared with day 2 (mean difference, 95% CI: 640–780 kJ; P < 0.001), equivalent to ~170 kcal (95% CI: 152, 185; P < 0.001). When examining the proportion of energy from macronutrients, no differences were observed for protein (mean difference: 0.01%; 95% CI: –0.65, 0.74; P = 0.90), carbohydrates (–0.03%; 95% CI: –1.05, 1.79; P = 0.61), or fiber (0.01%; 95% CI: –0.13, 0.10; P = 0.81). In contrast, fat contributed a significantly greater share of total energy intake on day 1 compared with day 2 (mean difference: 7.1 percentage points; 95% CI: 5.97, 8.18; P < 0.001). Taken together, higher day-1 energy intake was accompanied by a higher fat contribution, with no meaningful shifts in the relative contributions of protein, carbohydrates, or fiber. Macronutrient intake differences between GLP-1 and dual GIP/GLP-1 users Figure 4 shows the two-day average intake of macronutrients among participants receiving GLP-1 receptor agonist therapy versus dual GIP/GLP-1 therapy. Total protein intake was higher among those on dual GIP/GLP-1 compared with GLP-1 alone (mean difference: 4.29 g/day; 95% CI: 0.6, 7.9; P = 0.023). A similar difference was observed for animal-based protein, with participants on dual therapy consuming 5.42 g/day more (95% CI: 2.1, 8.7; P = 0.001). In contrast, plant-based protein intake did not differ significantly between groups, although regression analyses suggested a small decrease in the dual therapy group (–1.0 g/day; 95% CI: –2.1, –0.004; P = 0.049). Carbohydrate intake was significantly lower in the dual GIP/GLP-1 group, averaging ~16 g/day less than the GLP-1 group (95% CI: –24.0, –7.1; P < 0.001). Fiber intake was also slightly lower in the dual therapy group (–0.8 g/day; 95% CI: –1.5, –0.05; P = 0.037). No significant differences were found between groups for total fat or sodium intake (all P > 0.16). These adjusted estimates (linear models controlling for age and sex) were directionally consistent with unadjusted tests (e.g., total protein: t=–2.309, p=0.022; U=10374, p=0.005; animal-based protein: t=–3.055, p=0.003; U=9735.5, p<0.001), indicating higher total and animal-based protein but lower carbohydrate, and slightly lower fiber, among dual GIP/GLP-1 users. Predictors of weight reduction from multivariable models In multivariable models, treatment duration was the dominant predictor of greater weight loss ( Figure 5 ). Treatment duration was the strongest predictor: each additional week was associated with ~0.73 kg greater weight reduction (β = 0.73; 95% CI: 0.68, 0.78; P < 0.001). Male participants lost more weight than females (β = 2.61; 95% CI: 0.88, 4.34; P = 0.003). Older age was associated with smaller reductions (β = –0.08; 95% CI: –0.15, –0.01; P = 0.018). Dietary factors were also related to weight outcomes. Higher total protein intake was positively associated with weight loss (β = 0.45; 95% CI: 0.09, 0.80; P = 0.014), whereas animal-based protein showed an inverse association (β = –0.52; 95% CI: –0.87, –0.17; P = 0.004). Sodium intake was similarly associated with smaller reductions (β = –0.002; 95% CI: –0.00, –0.001; P = 0.013). The type of receptor agonist (GLP-1 vs. dual GIP/GLP-1) did not significantly predict weight reduction after adjustment for covariates (β = –0.99; 95% CI: –2.70, 0.71; P = 0.253). A second model, including energy intake and macronutrient distribution, explained a similar proportion of variance (R² = 0.73). In this model, a higher percentage of energy from protein was modestly associated with smaller weight reductions (β = –0.26; 95% CI: –0.52, –0.00; P = 0.047), while other dietary proportions (fat, carbohydrate, fiber) and total energy intake (kJ, kcal) were not significant predictors (all P ≥ 0.31). Overall explained variance was high for both models (R²=0.745 and 0.734; adjusted R²=0.737 and 0.727; p<0.001). Determinants of BMI reduction in multivariable analyses In adjusted models, treatment duration was the strongest predictor, with each additional week associated with ~0.25 units greater BMI reduction (β = 0.26; 95% CI: 0.24, 0.27; P < 0.001) ( Figure 6 ). Drug class (dual GIP/GLP-1 vs GLP-1) and sex were not associated with BMI change after adjustment (β=–0.36 to –0.47; P = 0.146–0.253; sex: β=–0.42 to –0.38; P = 0.191–0.257). Dietary variables showed modest associations. Sodium intake showed a small but significant inverse association with BMI reduction (β = –0.002; 95% CI: –0.00, –0.001; P = 0.003). Animal-based protein intake was inversely related to BMI reduction (β = –0.15; 95% CI: –0.27, –0.02; P = 0.024), while total protein showed only a borderline association (β = 0.12; 95% CI: –0.01, 0.25; P = 0.066). Other macronutrients, including fat, carbohydrate, and fiber, were not significantly associated with BMI change (all P > 0.05). In a second model including energy intake and macronutrient distribution, the percentage of energy derived from protein was negatively associated with BMI reduction (β = –0.10; 95% CI: –0.19, –0.01; P = 0.036). Neither total energy intake (kJ or kcal) nor the proportions from fat, carbohydrates, or fiber predicted BMI change (all P ≥ 0.26). Discussion The results of this study provide valuable information on the impact of treatment with GLP-1 receptor agonists and dual GIP/GLP-1 agonists on diet composition, calorie intake, and weight loss efficacy. The observed changes in dietary patterns and the identification of key dietary factors associated with weight loss shed light on mechanisms that may modulate the metabolic response in patients undergoing obesity pharmacotherapy. The problem of insufficient dietary support for patients has been recognized—the American College of Lifestyle Medicine, the American Society for Nutrition, the Obesity Medicine Association, and the Obesity Society recently published recommendations for nutritional support in GLP-1 therapy for obesity, indicating that although these drugs are remarkably effective, there are real challenges that require comprehensive nutritional support and lifestyle changes. In a study group of 387 adults treated with GLP-1 agonists or dual GIP/GLP-1 agonists, very low energy intake (753 kcal/day) and protein intake (33.4 g/day) were observed; less than 10% of participants met the recommendations for protein, fiber, vitamin D, calcium, and potassium intake. In addition, on weekdays, 170 kcal and 8–9 g of fat were consumed more than on weekends, indicating a rhythmic pattern of “Monday overeating – weekend malnutrition.” To date, studies on the diet of patients taking incretin have been few and limited mainly to energy analysis 3,24 ¹-³. Our results confirm previously observed energy deficits but document for the first time such profound protein and micronutrient deficiencies during pharmacotherapy, as well as differences between weekdays and weekends. 3 These findings are of significant clinical relevance, given that substantial calorie deficits, while effective for short-term fat loss, pose a risk of nutritional deficiencies, metabolic disorders, and adverse side effects such as sarcopenic obesity. 8,10,23 Sarcopenic obesity, defined as the coexistence of excessive obesity and reduced skeletal muscle mass and function, is particularly concerning. 3 This condition has serious consequences, including reduced physical performance, increased risk of falls and fractures, poorer quality of life, insulin resistance, and increased cardiovascular risk. Muscle loss during weight reduction is well documented, and without appropriate interventions, up to 25–30% of lost body weight may come from lean muscle tissue. [68] In the context of GLP-1 therapy, intense calorie restriction, if not adequately balanced with adequate protein intake and physical activity, may exacerbate muscle loss.[64,65] Patients treated with GLP-1 analogues may be at increased risk due to the marked appetite-suppressing effect of these drugs, which may reduce protein intake below recommended levels. 3,24,25 The effect of protein quality is consistent with meta-analyses indicating that a higher proportion of plant protein correlates with greater weight loss and less loss of lean body mass ⁴,⁵. The unexpected negative association of animal protein may reflect a simultaneous increase in sodium and saturated fat intake or limited physical activity, which requires further investigation. Given the observed average intake of only 33.4 g of protein/day and 7.2 g of fiber/day, routine dietary intervention to ensure adequate protein and micronutrient intake should be standard care before and during incretin therapy. Recommendations may include: (1) achieving a target protein intake of ≥ 1.2 g protein/kg of target body weight (approximately 75-90 g/day for most patients), (2) supplementation with key micronutrients, including vitamin D, calcium, and potassium, (3) a meal plan that limits fluctuations between days of the week, individualized meal planning to minimize fluctuations in diet between weekdays and weekends, and (4) regular monitoring of sodium levels. This comprehensive nutritional approach may reduce the risk of sarcopenia, loss of lean body mass, functional impairment, and weight regain after discontinuation of pharmacotherapy. Multiple regression analysis confirmed that the strongest predictor of weight loss was longer treatment duration rather than a specific type of therapy (GLP-1 vs. dual GIP/GLP-1), which is consistent with previous observations that the appetite-suppressing effects of GLP-1 analogs tend to wane over time, requiring long-term dietary and behavioral interventions to maintain efficacy.[52] Interestingly, while higher total protein intake was positively correlated with successful weight loss, animal protein showed a negative correlation. This finding suggests that the source and quality of protein have a significant impact on weight control outcomes, possibly due to differences in bioavailability, thermic effect, sodium content, or impact on gut microbiota, which requires further investigation.⁴,⁵ The significantly lower carbohydrate and fiber intake observed in patients treated with dual GIP/GLP-1 therapy, despite higher protein intake, indicates potential deficiencies in diet quality that may impact metabolic health. Dietary fiber plays a well-known role in glycemic control and weight management, highlighting the need for qualitative dietary monitoring in addition to pharmacological treatment. Low energy and nutrient intake across the group, particularly protein, fiber, and key micronutrients, highlights the importance of closely monitoring the nutritional status of patients receiving GLP-1 therapy. This monitoring is critical to minimizing the risk of complications associated with malnutrition. It is recommended that pharmacological care be combined with intensive dietary support and patient education, especially at the start and during incretin-based therapy. Identified nutritional deficiencies should be monitored individually and addressed through multidisciplinary interventions involving physicians and dietitians to optimize treatment outcomes, patient functioning, and overall quality of life. Further prospective studies involving objective assessment of body composition and monitoring of physical activity are needed to establish optimal nutritional guidelines to support long-term incretin therapy and prevent sarcopenic obesity. 3,25 3 8,11,24 Our study had both strengths and weaknesses. A key strength is the comprehensive evaluation of macronutrient intake and anthropometric outcomes in relation to GLP-1 and dual GIP/GLP-1 receptor agonist therapies in a substantial cohort of adults with obesity (N=387). The dietary assessment captured two consecutive days, one weekday and one weekend day, allowing nuanced comparisons of routine and leisure-period intake. High-quality data collection procedures, including participant-driven validation of dietary records, further strengthen reliability and support broader generalizability. In addition, the use of complementary analytic frameworks, classical frequentist statistics alongside Bayesian approaches, provides convergent evidence and richer inference about dietary intake and body-composition changes during pharmacotherapy for obesity. Important limitations should also be noted. Dietary intake was self-reported via food diaries and is therefore vulnerable to recall bias and under- or over-reporting, particularly among individuals with obesity. Additionally, dietary intake was recorded only over two days, limiting the ability to generalize dietary patterns beyond this short time-frame. Despite rigorous statistical adjustments, the observational design precludes definitive causal inferences about the relationships observed. Furthermore, participant recruitment through social media and internet forums may introduce selection bias, potentially limiting the representativeness of the general population with obesity. The lack of long-term follow-up beyond the end of treatment constrains our understanding of sustained dietary behavior changes and weight management outcomes. In conclude adults receiving incretin pharmacotherapy were found to have very low energy, protein, and key micronutrient intakes and marked differences between weekdays and weekends. The integration of dietary care aimed at adequate protein and micronutrient intake should accompany GLP-1/GIP-GLP-1 treatment to maximize weight loss efficacy and minimize the risk of sarcopenia. Further prospective studies with body composition measurements and physical activity monitoring are needed to determine the optimal composition of a diet supporting incretin therapy. Abbreviations BMI body mass index GLP-1 -glucagon-like peptide-1 GIP - gastric inhibitory polypeptide RDA - recommended dietary Allowance Declarations Data availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Statement of authors’ contributions to manuscript. Sebastian Korus ( SK) writing Danuta Cembrowska-Lech ( DCL) writing and statistical analysis. Karolina Kłoda (KK) writing and editing medical data Ewa Stachowska (ES)project conception , , writing References le Roux CW, Astrup A, Fujioka K, et al. 3 years of liraglutide versus placebo for type 2 diabetes risk reduction and weight management in individuals with prediabetes: a randomised, double-blind trial. 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Weight loss efficiency and safety of tirzepatide: A Systematic review. PLoS ONE. 2023;18(5):e0285197. 10.1371/journal.pone.0285197 . Ryan DH, Lingvay I, Deanfield J, et al. Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial. Nat Med. 2024;30(7):2049–57. 10.1038/s41591-024-02996-7 . Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Major revision 11 Nov, 2025 Reviewers agreed at journal 28 Sep, 2025 Reviewers invited by journal 28 Sep, 2025 Editor assigned by journal 18 Sep, 2025 First submitted to journal 17 Sep, 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. 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1","display":"","copyAsset":false,"role":"figure","size":60105,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of carbohydrate, fat, protein, fiber, and sodium intakes relative to Dietary Reference Intakes (DRI) across two 24-h dietary records. Bars indicate the percentage of participants below, meeting, or above the reference value (n = 387).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/340b38412d3e65b351ba754c.png"},{"id":93251750,"identity":"acead6c6-038c-44f8-a1d3-4acf8f59f8c1","added_by":"auto","created_at":"2025-10-10 15:52:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61303,"visible":true,"origin":"","legend":"\u003cp\u003eMean differences in macronutrient intake between day 1 and day 2 of the final intervention week. Points show mean differences with 95% CIs from paired t-tests; the table reports Cohen’s d and p-values (n=387). Positive values indicate higher intake on day 1.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/f6b6d09080652a48072d0a1b.png"},{"id":93250646,"identity":"1f981a9c-49f0-42e2-9566-008667e794b4","added_by":"auto","created_at":"2025-10-10 15:44:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65349,"visible":true,"origin":"","legend":"\u003cp\u003eMean differences in total energy intake and percentage of energy from macronutrients between day 1 and day 2 of the final intervention week. Points denote mean differences with 95% CIs from paired t-tests (n=387); positive values indicate higher intake or distribution on day 1.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/dc8eb97f4d6968f026d0aa53.png"},{"id":93252665,"identity":"3914063f-67fd-4af8-a76f-2185012bddb4","added_by":"auto","created_at":"2025-10-10 16:00:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160293,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-day average macronutrient intake by medication class (GLP-1 RA vs dual GIP/GLP-1) with age- and sex-adjusted differences. Left panels: distribution of 2-day mean intakes. Right panels: regression coefficients (dual GIP/GLP-1 vs GLP-1) with 95% CIs; positive values indicate higher intake with dual GIP/GLP-1 (n=387). RA, receptor agonist.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/caa46d016cbc38ddd57967be.png"},{"id":93250649,"identity":"6e5799c3-2d1e-4184-9178-a7d652bb55e3","added_by":"auto","created_at":"2025-10-10 15:44:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91740,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable predictors of weight reduction: models with macronutrient intake and energy distribution. Points show regression coefficients (β) with 95% CIs for associations with total weight loss (kg); positive β indicates greater loss. Covariates include drug class (dual GIP/GLP-1 vs GLP-1), diet variables, age, sex, and treatment duration (n=387).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/c7693cb6e9eda27d436f856a.png"},{"id":93251751,"identity":"7c190eb1-8022-4497-ac76-56ea6a2837fb","added_by":"auto","created_at":"2025-10-10 15:52:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96054,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable predictors of BMI reduction: models with macronutrient amounts and energy distribution. Forest plots display regression coefficients (β) and 95% CIs for associations with total BMI reduction; positive β denotes greater reduction. Covariates include drug class (dual GIP/GLP-1 vs GLP-1), diet variables, age, sex, and treatment duration (n=387).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/b45fa3004c35d1fe209a6ec9.png"},{"id":106809513,"identity":"3d5bb985-31fc-4ae3-b563-c03f601d740e","added_by":"auto","created_at":"2026-04-13 16:11:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1109550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7640335/v1/910a116f-c78f-429a-8196-71a1c1283b76.pdf"}],"financialInterests":"","formattedTitle":"Dietary intake patterns and nutritional adequacy among adults with overweight or obesity treated with GLP-1 or dual GIP/GLP-1 receptor agonists","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGLP-1 receptor agonists and dual GIP/GLP-1 agonists are currently the mainstay of pharmacotherapy for obesity and type 2 diabetes.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e By delaying gastric emptying and increasing satiety, these drugs enable significant weight loss and improve cardiovascular and metabolic risk\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. By influencing the hunger and satiety centers in the hypothalamus and the reward system, these drugs enable significant weight loss and improve cardiovascular and metabolic risk also through their direct effects.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e 1 However, the same mechanisms that promote energy deficit can lead to chronic protein and micronutrient deficiencies, increasing the risk of sarcopenia, impaired wound healing, and reduced immunity\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. It is estimated that 9\u0026ndash;12\u0026nbsp;million people worldwide are currently using GLP-1-based therapy, but their usual diet has not yet been systematically assessed.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have focused mainly on weight loss and glycemic control; few describe the macronutrient and micronutrient profile during therapy, and none compare intake on weekdays and weekends\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. There is also a lack of data on whether treatment duration, drug class (GLP-1 vs. GIP/GLP-1), or qualitative diet composition modify weight loss efficacy.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Rapid loss of lean body mass is an independent risk factor for sarcopenia, cholelithiasis, recurrence of metabolic disorders, and weight regain after drug discontinuation.\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe aim of this study was to assess energy intake and macro- and micronutrient intake on weekdays and weekends in adults reporting regular use of GLP-1 or GIP/GLP-1 agonists. The hypotheses were that (1) most patients do not meet the recommended intake standards for protein and key micronutrients, and (2) higher total protein intake, but not a predominance of animal protein, independently predicts greater weight loss, after adjusting for drug class and duration of therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThe study was designed as a \u003cb\u003eretrospective observational cohort\u003c/b\u003e conducted exclusively online, in accordance with the STROBE guidelines for observational epidemiological studies. The analysis includes users of GLP-1 receptor agonists (dulaglutide, liraglutide, semaglutide) or dual GIP/GLP-1 agonists (tirzepatide) recruited from Polish online communities. The project was approved by the Bioethics Committee of the Pomeranian Medical University, which determined that an anonymous survey did not require full review. All participants provided their informed consent electronically.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData source\u003c/h3\u003e\n\u003cp\u003eThe data was collected using a Google Forms form hosted on a secure researcher account with two-factor authentication. The anonymized data sets were stored and analyzed exclusively locally; the identifiers corresponded to random codes assigned to the respondents, which complies with the requirements of the GDPR. Participants were recruited between April 2024 and February 2025. Recruitment channels included targeted posts in Polish-language support groups on Facebook\u0026trade; and Instagram\u0026trade; for people with obesity and/or diabetes using incretins. The inclusion criteria for the study were: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, current use of GLP-1 or GLP-1 and GIP analogues, pharmacological treatment for obesity or metabolic disorders, and informed consent to participate in the study. Minors, individuals not taking the listed medications, or those who did not complete the questionnaire were excluded from participation.\u003c/p\u003e\u003cp\u003eAnthropometric data (body weight, height) were collected before the start of therapy and at the time of the study, along with a complete 48-hour weighted food diary (typical weekday and weekend).\u003c/p\u003e\u003cp\u003eThe final sample included 387 adults (80.4% women; mean age 34.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 years; baseline BMI 36.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0 kg/m\u0026sup2;). Types of drugs used by respondents: GLP-1 RA (dulaglutide, liraglutide, semaglutide) vs. dual GIP/GLP-1 agonist (tirzepatide). Average duration of therapy reported by respondents was 15.7 (SD 14 weeks).\u003c/p\u003e\u003cp\u003eThe study collected information on dietary profiles. The form consisted of two parts covering demographic data \u0026ndash; age, gender, body weight before therapy and current body weight, height, duration of drug use (weeks), type of preparation, and any bariatric surgery.\u003c/p\u003e\u003cp\u003eA 48-hour food diary \u0026ndash; weighted, covering one typical working day and one weekend day; respondents recorded the time, weight or household measure, method of preparation, and brand of the product. Before sending the form, the system checked for completeness, and the user could correct any omissions.\u003c/p\u003e\u003cp\u003eRespondents recorded in detail all food items with weighed portions or household measures for two days: a working day and a weekend day. The records were transferred to the Dieta-6 software (Poland, NIZP PHZ-PIB 2024 database), which generated values for energy and 42 nutrients. The results were compared with current Polish nutritional standards. Data were obtained on the average daily intake of macronutrients and key micronutrients (total protein, animal/plant protein, fiber, calcium, potassium, vitamins D, E, C) on weekdays and weekends.\u003c/p\u003e\u003cp\u003e The primary endpoints in this study were to estimate the percentage of participants meeting the RDA/AI standards for protein and selected micronutrients, and the difference in energy and macronutrient intake between weekdays and weekends. Secondary endpoints were the reported change in body weight (kg) and BMI from the start of treatment.\u003c/p\u003e\u003cp\u003e The study complies with the principles of the Declaration of Helsinki. Anonymization prevents the identification of individuals, therefore, in analogy to the JAMA study, no additional consent was required for the sharing of secondary data. The results of diet adequacy and body weight/BMI changes were analyzed separately.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eOutcomes\u003c/h2\u003e\u003cp\u003eAdequacy of intake included consumption of carbohydrates, total fat, fiber, protein (total and divided into animal/plant sources) and sodium from Day 1, Day 2, and their average was compared with the Polish Nutrition Standards 2024 (DRI). For each of the five food categories, each observation was classified as deficit \u0026ndash; value\u0026thinsp;\u0026lt;\u0026thinsp;lower limit of DRI, balanced \u0026ndash; value within DRI, excess \u0026ndash; value\u0026thinsp;\u0026gt;\u0026thinsp;upper limit of DRI.\u003c/p\u003e\u003cp\u003eFrom these classifications, the percentage of participants in each category was calculated for Day 1, Day 2 and the two-day average; these results were visualized to show the degree of compliance with national recommendations.\u003c/p\u003e\u003cp\u003eThe primary outcome was the proportion of participants in the \u0026ldquo;balanced\u0026rdquo; category for each component at the three measurement points. Secondary outcomes included (a) the difference in energy, macronutrient, and fiber intake between weekdays and weekends;\u003c/p\u003e\u003cp\u003eself-reported change in body weight (kg) and BMI from the start of incretin therapy, calculated using the formula\u003c/p\u003e\u003cp\u003e% difference\u0026thinsp;=\u0026thinsp;100 \u0026times; ((day 2\u0026thinsp;\u0026minus;\u0026thinsp;day 1) / day 1).\u003c/p\u003e\u003cp\u003eThe distributions of key variables were assessed visually (histograms, box plots) and based on skewness; variables showing significant asymmetry were log-transformed or standardized. For paired comparisons (Day 1 vs. Day 2), a t-test for dependent samples was used. The relationship between body weight/BMI changes and dietary factors, age, and gender was assessed using an OLS regression model; β coefficients, 95% CI, and p-values were reported; p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStudy population baseline characteristics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean or N (SD or %)\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;387)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, F / M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e311 (80.4) / 76 (19.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.9 (9.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline weight, kg\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.4 (16.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.4 (5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline BMI class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Overweight: 27\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (5.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Obesity class 1 (30 to \u0026lt;\u0026thinsp;35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153 (39.53)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Obesity class 2 (35 to \u0026lt;\u0026thinsp;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (35.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Obesity class 3 (\u0026ge;\u0026thinsp;40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (19.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndex medication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e387 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Dulaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (0.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Liraglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88 (22.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Semaglutide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (54.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026bull; Tirzepatide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (22.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBariatric surgery history\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eValues are unadjusted means (SD) or n (%). Abbreviations: F, female; M, male; BMI, body mass index.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed to evaluate changes between day 1 and day 2 and to examine how dietary intake related to observed differences in body weight and BMI. For each participant, absolute and percentage changes were computed, with percentage change defined as 100 \u0026times; ((day 2 \u0026ndash; day 1)/day 1). Data distributions for body weight, macronutrient intake, and their day-to-day changes were assessed visually and with the Shapiro\u0026ndash;Wilk test. Normally distributed variables were compared using paired t-tests, whereas non-normally distributed variables were evaluated with the Wilcoxon signed-rank test.\u003c/p\u003e\u003cp\u003eAssociations between weight or BMI changes and dietary intake were further explored using ordinary least squares regression. Models included absolute changes in macronutrients (protein, fat, carbohydrate, fiber, and sodium), age, sex, treatment time, and receptor agonist use as covariates. In sensitivity analyses, macronutrient intake was expressed as percentage of total energy. Regression coefficients (β) with 95% CIs are reported.\u003c/p\u003e\u003cp\u003eTo contextualize dietary patterns, intakes of macronutrients and sodium were compared against the Polish Dietary Reference Intakes and classified as below, within, or above the recommended ranges. The proportions of participants in each category were calculated for day 1, day 2, and the average of both days.\u003c/p\u003e\u003cp\u003eAll analyses were conducted using Python (v3.10; statsmodels package). Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (2-sided).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDietary intake patterns compared with Polish Dietary Reference Intakes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e shows the distribution of carbohydrate, fat, protein, fiber, and sodium intakes relative to Dietary Reference Intakes across two 24-h dietary records. Analysis of two-day dietary records showed that approximately half of participants consumed carbohydrates within the recommended range on both days (51\u0026ndash;53%) and when averaged across days (59%). About one-third consumed less than recommended, while 6\u0026ndash;10% exceeded the upper limit.\u003c/p\u003e\n\u003cp\u003eFat intake varied between days. On day 1, participants were evenly distributed across categories of below (37%), within (34%), and above (29%) the Adequate Intake (AI). By day 2, nearly two-thirds consumed less than the AI, \u0026lt;30% met the guideline, and ~7% exceeded it. Based on the two-day average, 38% met the recommendation, 49% consumed less, and 13% exceeded it.\u003c/p\u003e\n\u003cp\u003eFiber intake was markedly inadequate. The mean intake was 7.2 g/day, and no participant met the recommendation of \u0026ge;25 g/day.\u003c/p\u003e\n\u003cp\u003eProtein intake was also substantially below the Recommended Dietary Allowance (RDA). Across both days, ~97% failed to meet the requirement, while only 2\u0026ndash;4% exceeded it. None met the exact RDA.\u003c/p\u003e\n\u003cp\u003eSodium intake was more aligned with recommendations: 72\u0026ndash;85% of participants were at or near the AI, while 15\u0026ndash;28% exceeded it; no participant consumed less than the guideline.\u003c/p\u003e\n\u003cp\u003eTo sum up, this profile points to a carbohydrate-adequate but protein- and fiber-poor diet with unstable fat intake, highlighting protein and fiber (and, secondarily, fat) as primary targets for dietary improvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWithin-week day-to-day variability in macronutrient intake\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePaired analyses compared nutrient intake between day 1 and day 2 of the final intervention week. \u0026nbsp;Across the two consecutive recall days in the final week, most macronutrient intakes were stable (\u003cstrong\u003eFigure 2\u003c/strong\u003e). For protein, total intake did not differ between days (mean difference = 0.02 g; 95% CI: \u0026ndash;0.88, 1.68; P = 0.54). Similarly, no differences were observed for plant-based protein (0.02 g; 95% CI: \u0026ndash;0.48, 0.64; P = 0.78). By contrast, animal-based protein intake was significantly higher on day 1 than on day 2 (0.56 g; 95% CI: 7.34, 9.16; P \u0026lt; 0.001). Fat intake also differed between days, with participants consuming 7\u0026ndash;10 g more on day 1 than on day 2 (0.60 g; 95% CI: 7.25, 9.64; P \u0026lt; 0.001). In contrast, carbohydrate and fiber intakes were stable across days, with no significant mean differences [carbohydrates: 0.02 g; 95% CI: \u0026ndash;4.83, 3.23; P = 0.71; fiber: 0.01 g; 95% CI: \u0026ndash;0.37, 0.36; P = 0.98]. Sodium intake was substantially higher on day 1, averaging ~370 mg more than on day 2 (0.56 mg; 95% CI: 316.76, 435.93; P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTaken together, these results indicate that the day-to-day variation in protein intake reflected shifts in protein sources rather than total protein, and that higher day-1 sodium accompanied higher fat intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDay-to-day differences in energy intake and macronutrient distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e, participants consumed significantly more total energy on day 1 compared with day 2, whereas the proportion of energy from protein, carbohydrate, and fiber did not differ between days. Paired analyses showed that total energy intake was significantly higher on day 1 compared with day 2 (mean difference, 95% CI: 640\u0026ndash;780 kJ; P \u0026lt; 0.001), equivalent to ~170 kcal (95% CI: 152, 185; P \u0026lt; 0.001). When examining the proportion of energy from macronutrients, no differences were observed for protein (mean difference: 0.01%; 95% CI: \u0026ndash;0.65, 0.74; P = 0.90), carbohydrates (\u0026ndash;0.03%; 95% CI: \u0026ndash;1.05, 1.79; P = 0.61), or fiber (0.01%; 95% CI: \u0026ndash;0.13, 0.10; P = 0.81). In contrast, fat contributed a significantly greater share of total energy intake on day 1 compared with day 2 (mean difference: 7.1 percentage points; 95% CI: 5.97, 8.18; P \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eTaken together, higher day-1 energy intake was accompanied by a higher fat contribution, with no meaningful shifts in the relative contributions of protein, carbohydrates, or fiber.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacronutrient intake differences between GLP-1 and dual GIP/GLP-1 users\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e shows the two-day average intake of macronutrients among participants receiving GLP-1 receptor agonist therapy versus dual GIP/GLP-1 therapy. Total protein intake was higher among those on dual GIP/GLP-1 compared with GLP-1 alone (mean difference: 4.29 g/day; 95% CI: 0.6, 7.9; P = 0.023). A similar difference was observed for animal-based protein, with participants on dual therapy consuming 5.42 g/day more (95% CI: 2.1, 8.7; P = 0.001). In contrast, plant-based protein intake did not differ significantly between groups, although regression analyses suggested a small decrease in the dual therapy group (\u0026ndash;1.0 g/day; 95% CI: \u0026ndash;2.1, \u0026ndash;0.004; P = 0.049). Carbohydrate intake was significantly lower in the dual GIP/GLP-1 group, averaging ~16 g/day less than the GLP-1 group (95% CI: \u0026ndash;24.0, \u0026ndash;7.1; P \u0026lt; 0.001). Fiber intake was also slightly lower in the dual therapy group (\u0026ndash;0.8 g/day; 95% CI: \u0026ndash;1.5, \u0026ndash;0.05; P = 0.037). No significant differences were found between groups for total fat or sodium intake (all P \u0026gt; 0.16).\u003c/p\u003e\n\u003cp\u003eThese adjusted estimates (linear models controlling for age and sex) were directionally consistent with unadjusted tests (e.g., total protein: t=\u0026ndash;2.309, p=0.022; U=10374, p=0.005; animal-based protein: t=\u0026ndash;3.055, p=0.003; U=9735.5, p\u0026lt;0.001), indicating higher total and animal-based protein but lower carbohydrate, and slightly lower fiber, among dual GIP/GLP-1 users.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of weight reduction from multivariable models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn multivariable models, treatment duration was the dominant predictor of greater weight loss (\u003cstrong\u003eFigure 5\u003c/strong\u003e). Treatment duration was the strongest predictor: each additional week was associated with ~0.73 kg greater weight reduction (\u0026beta; = 0.73; 95% CI: 0.68, 0.78; P \u0026lt; 0.001). Male participants lost more weight than females (\u0026beta; = 2.61; 95% CI: 0.88, 4.34; P = 0.003). Older age was associated with smaller reductions (\u0026beta; = \u0026ndash;0.08; 95% CI: \u0026ndash;0.15, \u0026ndash;0.01; P = 0.018).\u003c/p\u003e\n\u003cp\u003eDietary factors were also related to weight outcomes. Higher total protein intake was positively associated with weight loss (\u0026beta; = 0.45; 95% CI: 0.09, 0.80; P = 0.014), whereas animal-based protein showed an inverse association (\u0026beta; = \u0026ndash;0.52; 95% CI: \u0026ndash;0.87, \u0026ndash;0.17; P = 0.004). Sodium intake was similarly associated with smaller reductions (\u0026beta; = \u0026ndash;0.002; 95% CI: \u0026ndash;0.00, \u0026ndash;0.001; P = 0.013).\u003c/p\u003e\n\u003cp\u003eThe type of receptor agonist (GLP-1 vs. dual GIP/GLP-1) did not significantly predict weight reduction after adjustment for covariates (\u0026beta; = \u0026ndash;0.99; 95% CI: \u0026ndash;2.70, 0.71; P = 0.253).\u003c/p\u003e\n\u003cp\u003eA second model, including energy intake and macronutrient distribution, explained a similar proportion of variance (R\u0026sup2; = 0.73). In this model, a higher percentage of energy from protein was modestly associated with smaller weight reductions (\u0026beta; = \u0026ndash;0.26; 95% CI: \u0026ndash;0.52, \u0026ndash;0.00; P = 0.047), while other dietary proportions (fat, carbohydrate, fiber) and total energy intake (kJ, kcal) were not significant predictors (all P \u0026ge; 0.31).\u003c/p\u003e\n\u003cp\u003eOverall explained variance was high for both models (R\u0026sup2;=0.745 and 0.734; adjusted R\u0026sup2;=0.737 and 0.727; p\u0026lt;0.001).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeterminants of BMI reduction in multivariable analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn adjusted models, treatment duration was the strongest predictor, with each additional week associated with ~0.25 units greater BMI reduction (\u0026beta; = 0.26; 95% CI: 0.24, 0.27; P \u0026lt; 0.001) (\u003cstrong\u003eFigure 6\u003c/strong\u003e). Drug class (dual GIP/GLP-1 vs GLP-1) and sex were not associated with BMI change after adjustment (\u0026beta;=\u0026ndash;0.36 to \u0026ndash;0.47; P = 0.146\u0026ndash;0.253; sex: \u0026beta;=\u0026ndash;0.42 to \u0026ndash;0.38; P = 0.191\u0026ndash;0.257).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDietary variables showed modest associations. Sodium intake showed a small but significant inverse association with BMI reduction (\u0026beta; = \u0026ndash;0.002; 95% CI: \u0026ndash;0.00, \u0026ndash;0.001; P = 0.003). Animal-based protein intake was inversely related to BMI reduction (\u0026beta; = \u0026ndash;0.15; 95% CI: \u0026ndash;0.27, \u0026ndash;0.02; P = 0.024), while total protein showed only a borderline association (\u0026beta; = 0.12; 95% CI: \u0026ndash;0.01, 0.25; P = 0.066). Other macronutrients, including fat, carbohydrate, and fiber, were not significantly associated with BMI change (all P \u0026gt; 0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a second model including energy intake and macronutrient distribution, the percentage of energy derived from protein was negatively associated with BMI reduction (\u0026beta; = \u0026ndash;0.10; 95% CI: \u0026ndash;0.19, \u0026ndash;0.01; P = 0.036). Neither total energy intake (kJ or kcal) nor the proportions from fat, carbohydrates, or fiber predicted BMI change (all P \u0026ge; 0.26).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study provide valuable information on the impact of treatment with GLP-1 receptor agonists and dual GIP/GLP-1 agonists on diet composition, calorie intake, and weight loss efficacy. The observed changes in dietary patterns and the identification of key dietary factors associated with weight loss shed light on mechanisms that may modulate the metabolic response in patients undergoing obesity pharmacotherapy. The problem of insufficient dietary support for patients has been recognized—the American College of Lifestyle Medicine, the American Society for Nutrition, the Obesity Medicine Association, and the Obesity Society recently published recommendations for nutritional support in GLP-1 therapy for obesity, indicating that \u0026nbsp;although these drugs are remarkably effective, there are real challenges that require comprehensive nutritional support and lifestyle changes.\u003c/p\u003e\n\u003cp\u003eIn a study group of 387 adults treated with GLP-1 agonists or dual GIP/GLP-1 agonists, very low energy intake (753 kcal/day) and protein intake (33.4 g/day) were observed; less than 10% of participants met the recommendations for protein, fiber, vitamin D, calcium, and potassium intake. In addition, on weekdays, 170 kcal and 8–9 g of fat were consumed more than on weekends, indicating a rhythmic pattern of “Monday overeating – weekend malnutrition.”\u003c/p\u003e\n\u003cp\u003eTo date, studies on the diet of patients taking incretin have been few and limited mainly to energy analysis \u003csup\u003e3,24\u003c/sup\u003e¹-³. Our results confirm previously observed energy deficits but document for the first time such profound protein and micronutrient deficiencies during pharmacotherapy, as well as differences between weekdays and weekends.\u003csup\u003e3\u003c/sup\u003e These findings are of significant clinical relevance, given that substantial calorie deficits, while effective for short-term fat loss, pose a risk of nutritional deficiencies, metabolic disorders, and adverse side effects such as sarcopenic obesity.\u003csup\u003e8,10,23\u003c/sup\u003e Sarcopenic obesity, defined as the coexistence of excessive obesity and reduced skeletal muscle mass and function, is particularly concerning.\u003csup\u003e3\u003c/sup\u003e This condition has serious consequences, including reduced physical performance, increased risk of falls and fractures, poorer quality of life, insulin resistance, and increased cardiovascular risk. Muscle loss during weight reduction is well documented, and without appropriate interventions, up to 25–30% of lost body weight may come from lean muscle tissue. [68] In the context of GLP-1 therapy, intense calorie restriction, if not adequately balanced with adequate protein intake and physical activity, may exacerbate muscle loss.[64,65] Patients treated with GLP-1 analogues may be at increased risk due to the marked appetite-suppressing effect of these drugs, which may reduce protein intake below recommended levels.\u003csup\u003e3,24,25\u003c/sup\u003e The effect of protein quality is consistent with meta-analyses indicating that a higher proportion of plant protein correlates with greater weight loss and less loss of lean body mass ⁴,⁵. The unexpected negative association of animal protein may reflect a simultaneous increase in sodium and saturated fat intake or limited physical activity, which requires further investigation.\u003c/p\u003e\n\u003cp\u003eGiven the observed average intake of only 33.4 g of protein/day and 7.2 g of fiber/day, routine dietary intervention to ensure adequate protein and micronutrient intake should be standard care before and during incretin therapy. Recommendations may include: (1) achieving a target protein intake of ≥ 1.2 g protein/kg of target body weight (approximately 75-90 g/day for most patients), (2) supplementation with key micronutrients, including vitamin D, calcium, and potassium, (3) a meal plan that limits fluctuations between days of the week, individualized meal planning to minimize fluctuations in diet between weekdays and weekends, and (4) regular monitoring of sodium levels. This comprehensive nutritional approach may \u0026nbsp; reduce the risk of sarcopenia, loss of lean body mass, functional impairment, and weight regain after discontinuation of pharmacotherapy.\u003c/p\u003e\n\u003cp\u003eMultiple regression analysis confirmed that the strongest predictor of weight loss was longer treatment duration rather than a specific type of therapy (GLP-1 vs. dual GIP/GLP-1), which is consistent with previous observations that the appetite-suppressing effects of GLP-1 analogs tend to wane over time, requiring long-term dietary and behavioral interventions to maintain efficacy.[52] Interestingly, while higher total protein intake was positively correlated with successful weight loss, animal protein showed a negative correlation. This finding suggests that the source and quality of protein have a significant impact on weight control outcomes, possibly due to differences in bioavailability, thermic effect, sodium content, or impact on gut microbiota, which requires further investigation.⁴,⁵\u003c/p\u003e\n\u003cp\u003eThe significantly lower carbohydrate and fiber intake observed in patients treated with dual GIP/GLP-1 therapy, despite higher protein intake, indicates potential deficiencies in diet quality that may impact metabolic health. Dietary fiber plays a well-known role in glycemic control and weight management, highlighting the need for qualitative dietary monitoring in addition to pharmacological treatment.\u003c/p\u003e\n\u003cp\u003eLow energy and nutrient intake across the group, particularly protein, fiber, and key micronutrients, highlights the importance of closely monitoring the nutritional status of patients receiving GLP-1 therapy. This monitoring is critical to minimizing the risk of complications associated with malnutrition. It is recommended that pharmacological care be combined with intensive dietary support and patient education, especially at the start and during incretin-based therapy. Identified nutritional deficiencies should be monitored individually and addressed through multidisciplinary interventions involving physicians and dietitians to optimize treatment outcomes, patient functioning, and overall quality of life.\u003c/p\u003e\n\u003cp\u003eFurther prospective studies involving objective assessment of body composition and monitoring of physical activity are needed to establish optimal nutritional guidelines to support long-term incretin therapy and prevent sarcopenic obesity.\u003csup\u003e3,25\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e8,11,24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur study had both strengths and weaknesses. A key strength is the comprehensive evaluation of macronutrient intake and anthropometric outcomes in relation to GLP-1 and dual GIP/GLP-1 receptor agonist therapies in a substantial cohort of adults with obesity (N=387). The dietary assessment captured two consecutive days, one weekday and one weekend day, allowing nuanced comparisons of routine and leisure-period intake. High-quality data collection procedures, including participant-driven validation of dietary records, further strengthen reliability and support broader generalizability. In addition, the use of complementary analytic frameworks, classical frequentist statistics alongside Bayesian approaches, provides convergent evidence and richer inference about dietary intake and body-composition changes during pharmacotherapy for obesity.\u003c/p\u003e\n\u003cp\u003eImportant limitations should also be noted. Dietary intake was self-reported via food diaries and is therefore vulnerable to recall bias and under- or over-reporting, particularly among individuals with obesity. Additionally, dietary intake was recorded only over two days, limiting the ability to generalize dietary patterns beyond this short time-frame. Despite rigorous statistical adjustments, the observational design precludes definitive causal inferences about the relationships observed. Furthermore, participant recruitment through social media and internet forums may introduce selection bias, potentially limiting the representativeness of the general population with obesity. The lack of long-term follow-up beyond the end of treatment constrains our understanding of sustained dietary behavior changes and weight management outcomes.\u003c/p\u003e\n\u003cp\u003eIn conclude adults receiving incretin pharmacotherapy were found to have very low energy, protein, and key micronutrient intakes and marked differences between weekdays and weekends. The integration of dietary care aimed at adequate protein and micronutrient intake should accompany GLP-1/GIP-GLP-1 treatment to maximize weight loss efficacy and minimize the risk of sarcopenia. Further prospective studies with body composition measurements and physical activity monitoring are needed to determine the optimal composition of a diet supporting incretin therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eGLP-1 -glucagon-like peptide-1 \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGIP - gastric inhibitory polypeptide\u003c/p\u003e\n\u003cp\u003eRDA - recommended dietary Allowance\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of authors’ contributions to manuscript.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSebastian Korus ( SK)\u0026nbsp;writing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDanuta Cembrowska-Lech ( DCL) \u0026nbsp;writing and \u0026nbsp;statistical analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Karolina Kłoda (KK) writing and editing medical data\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ewa Stachowska (ES)project conception\u003csup\u003e,\u003c/sup\u003e, writing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ele Roux CW, Astrup A, Fujioka K, et al. 3 years of liraglutide versus placebo for type 2 diabetes risk reduction and weight management in individuals with prediabetes: a randomised, double-blind trial. 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Nat Med. 2024;30(7):2049\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41591-024-02996-7\u003c/span\u003e\u003cspan address=\"10.1038/s41591-024-02996-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7640335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7640335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eGLP-1 dual GIP/GLP-1 agonists significantly suppress appetite, but it is unclear whether the typical diet of patients treated with these drugs still meets their basic nutritional needs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo assess nutrients intake among adults undergoing pharmacotherapy and identify dietary predictors of weight loss efficacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective cohort study was conducted online among social media support groups (April 2024–February 2025). Participants included 387 adults, who reported regular once-weekly use of a GLP-1 agonist or dual GIP/GLP-1 agonist and completed 48-hour food diaries (one weekday and one weekend day). Daily energy and nutrient intake were reconstructed using a specialized software (Diet 6) containing an up-to-date database and standards issued by the National Institute of Public Health (NIZP PZH – PIB 2024). Differences between week and weekends were analyzed using a paired-samples t-test. Multiple linear regression assessed dietary and treatment predictors of weight loss.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAverage energy intake was 753 kcal (SD 257.8 kcal), with protein 33.4 g (SD 15.3 g), fat 26.5 g (SD 12.4 g), carbohydrates 96.4 g (SD 35.6 g), and fiber 7.2 g (SD 3.1 g). Less than 10 % of participants met the recommended intakes for protein. Weekday intake was significantly higher by 170 kcal (95% CI 152–185 kcal; p \u0026lt; 0.001), with greater consumption of fat (8–9 g), and sodium (370 mg). Higher total protein intake promoted weight loss (β = 0.446; p = 0.014), while \u003cdel\u003ea\u003c/del\u003ehigher proportion of animal protein (β = –0.517; p = 0.004) and higher sodium intake (β ≈ –0.002; p = 0.013) reduced it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eAdults receiving GLP-1 pharmacotherapy exhibit insufficient protein and critical micronutrients, alongside excess fat and sodium intake on weekdays. Routine dietary education emphasizing protein adequacy and micronutrient sufficiency should accompany incretin therapy to prevent nutritional deficiencies, sarcopenia, and optimize weight loss outcomes.\u003c/p\u003e","manuscriptTitle":"Dietary intake patterns and nutritional adequacy among adults with overweight or obesity treated with GLP-1 or dual GIP/GLP-1 receptor agonists","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-10 15:44:15","doi":"10.21203/rs.3.rs-7640335/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-11-11T20:42:13+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-28T18:42:36+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-28T11:33:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-18T14:43:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-09-17T08:40:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c8b6277-4cbd-43b8-86ab-e2b8ba24f69c","owner":[],"postedDate":"October 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:08:04+00:00","versionOfRecord":{"articleIdentity":"rs-7640335","link":"https://doi.org/10.1186/s12967-026-07702-4","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-04-11 15:59:05","publishedOnDateReadable":"April 11th, 2026"},"versionCreatedAt":"2025-10-10 15:44:15","video":"","vorDoi":"10.1186/s12967-026-07702-4","vorDoiUrl":"https://doi.org/10.1186/s12967-026-07702-4","workflowStages":[]},"version":"v1","identity":"rs-7640335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7640335","identity":"rs-7640335","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}