Ultra-Processed Food Intake is Associated with Altered Glucose Homeostasis in Young Adults

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Patterson, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6875960/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Nutrition & Metabolism → Version 1 posted 16 You are reading this latest preprint version Abstract Background: Ultra-processed foods (UPFs), often high in sodium, sugar, and unhealthy fats, compose more than half of total dietary energy consumption in the United States. A diet composed of a high amount of UPFs can contribute to glucose dysregulation and insulin resistance, which may lead to prediabetes and type 2 diabetes (T2D). The goal of this study is to examine associations between UPF consumption and prediabetes and related biomarkers in youth. Methods: Young adults (n = 85) aged 17–22 years old from the Meta-AIR study, a subset of the Children’s Health Study, were enrolled between 2014–2018 and returned for a second visit between 2020–2022. Participants completed two 24-hour dietary recalls and an oral glucose tolerance test at each visit. Food items were categorized as either an UPF or non-UPF according to NOVA classification guidelines. The proportion of the diet composed of UPFs was calculated for each participant. Regression models were used to assess relationships of UPF consumption at baseline and change between visits with markers of glucose homeostasis at follow-up, adjusting for demographics and physical activity. Results: A 10 percentage-point increase in UPF consumption between visits was associated with a 64% (OR: 1.64, 95% Cl: 1.15, 2.50) higher risk for prediabetes and 56% (OR: 1.56, 95% CI: 1.42, 5.86) higher risk for impaired glucose tolerance at follow-up. Higher baseline UPF consumption was significantly positively associated with fasting insulin ( β = 2.09, 95% CI: 0.06, 4.12), 2-hour insulin ( β = 44.75, 95% CI: 22.26, 67.25) and insulin area under the curve ( β = 63.19, 95% CI: 34.84, 91.54) at follow-up. Conclusion: UPF consumption may increase the risk for T2D among young adults. Our findings suggest that limiting UPF consumption could be an important strategy for T2D prevention in this population. ultra-processed food type 2 diabetes prediabetes young adults body composition Figures Figure 1 Introduction Prediabetes has become more common among young adults in recent years, which increases the risk for early onset type 2 diabetes (T2D) 1 , 2 . In the United States (US), the prevalence of diagnosed T2D is estimated to be about 48 per 100,000 in youth under 20 years of age 3 . T2D is also a significant global public health concern because it can affect individuals’ quality of life, lead to many comorbidities, and increase mortality risk 2 , 4 . The early onset of T2D among young adults can lead to more long-term health issues compared to onset of T2D in later adulthood 1 , 5 – 9 . Obesity greatly increases the risk for prediabetes and T2D, and poor diet and other lifestyle factors can be risk factors for all three conditions 2 , 4 , 5 . Since prediabetes, T2D, and obesity are closely related to each other and share similar risk factors, the assessment of modifiable risk factors like diet is crucial for prevention and treatment of these conditions. In the US, more than half of total dietary energy consumption is composed of ultra-processed foods (UPFs) 10 , 11 . UPFs are food items that go through multiple industrial processes before people purchase or eat them 12 . Examples of common UPFs include soft drinks, packaged snacks, margarine, and sausages 12 . Most UPFs are calorie-dense and high in sugar, salt, and unhealthy fats, while low in protein, vitamins, and minerals 10 , 13 – 16 . Studies have demonstrated that higher consumption of UPFs results in poor nutritional diet quality and increased risk for the development of chronic diseases, including T2D and hypertension 13 – 18 . It is important to limit UPF consumption in childhood and adolescence due to the high content of added sugar and saturated fats in UPFs and their possible contribution to weight gain, T2D, cardiovascular disease, and hypertension 16 – 18 . Though many risk factors for metabolic disease first appear in early life, most research studies have focused on the effects of UPFs on metabolic disease in middle-aged and older adults 19 – 22 . Many of these studies show that diets with a higher proportion of UPFs or increasing consumption of UPFs is associated with a higher risk of T2D and obesity among adults 19 – 22 . In addition, most previous studies examining UPF consumption and metabolic disease have been conducted in Brazil and primarily used cross-sectional analyses, with only a few using a longitudinal study design 23 – 27 . However, few studies have assessed the associations between UPFs and T2D or obesity in young people, and those that have are cross-sectional and report mixed results 18 , 20 – 22 . Some studies have shown that limiting the consumption of UPFs can reduce T2D and obesity risk among children and young adults, while others found no association between UPF consumption and obesity or overweight 18 , 20 – 22 . Because of the limited studies on UPF consumption among young adults, and the importance of early lifestyle changes in preventing T2D among high risk populations, more research is needed to understand the relationship between UPF consumption and risk for T2D and obesity in young adults 28 . The purpose of this study is to assess the longitudinal associations between UPF consumption and prediabetes and obesity in young adults, using glucose and insulin measurements, body composition, and diet assessment over four years of follow-up. We hypothesized that increases in UPF consumption would be associated with a higher risk of altered glucose homeostasis, insulin resistance, obesity, and prediabetes. Methods Cohort Between 2014–2018, 155 young adults aged 17–22 who had previously participated in the Children’s Health Study were invited to enroll in the Meta-AIR study 9 . To be eligible, participants had a history of overweight or obesity in early adolescence, were not diagnosed with either type 1 or type 2 diabetes, were not taking medications that influence glucose metabolism, and had no other significant medical diagnosis 9 . Of these, 85 returned for a follow-up visit between 2020–2022 (the MetaCHEM study, Fig. 1 ) 9 . This study was approved by the Institutional Review Board at the University of Southern California and written informed consent or assent were obtained from participants and their guardians. Dietary Assessment and UPF Classification Participants completed two non-consecutive 24-hour dietary recalls on one weekday and one weekend day at each visit 9 . Trained interviewers used the Nutritional Data System for Research (NDSR) software version 2014 to complete the baseline recalls, while participants used the Automated Self-Administered 24-hr Dietary Assessment Tool (ASA24) version 2018 to conduct the recalls at the follow-up visit 9 , 29 , 30 . At baseline, 10.3% (n = 16) of participants completed only one recall, while at follow up 10.2% (n = 9) of participant completed only one recall. In this study, a total of 1,167 unique food items were reported at the baseline visit and a total of 807 unique food items were reported the at the follow-up visit. Some food items reported at the follow-up visit contained mixed dishes with multiple ingredients. When possible, these foods were disaggregated into individual ingredients using the 2017–2018 Food and Nutrient Database for Dietary Studies (FNDDS) Ingredients database and matched to the food codes from the FNDDS Foods and Beverages database that were provided by ASA24 9,31 . Each ingredient or food was classified as ultra-processed or not by two independent reviewers (YL, EC). Foods were classified according to the NOVA group definitions of 1) unprocessed and minimally processed foods, 2) processed culinary ingredients, 3) processed foods, and 4) ultra-processed foods (UPFs) 10 , 12 , 32 . Any disagreements on UPF classification were resolved through discussion. Briefly, unprocessed and minimally processed foods in NOVA group 1 are edible parts of plants or animals and foods that have been through basic processing such as drying, griding and freezing (e.g., fruits and vegetables, eggs, meat and seafood, flour, spices) 12 . Processed culinary ingredients in NOVA group 2 mainly composed of vegetable oils, butter, salt, sugar, and honey for the main purpose of seasoning and cooking 12 . Processed foods in NOVA group 3 contain food products that are made by adding items in group 2 to group 1 for the purpose of preservation (e.g., canned vegetables, cured or smoked meat and fish, freshly made bread) 12 . UPFs in NOVA group 4 are made with series of industrial techniques that cannot be replicated at home and may involve molding or pre-frying, the addition of colors and flavors, and often includes added sugars, salt, oils, and fats 12 . This group also includes most branded and packaged foods, pre-prepared ready-to-eat products, and instant foods 12 . However, enriched food items, like milk or flour with added vitamins or minerals, are not included in this group. Examples of UPFs are soft drinks, candies, cereal, ice-cream, mass-produced cookies or pastries, margarines, packaged and shelf-stable spreads, milk drinks, flavored yogurts, pizza, and sausages. UPF classification was made with the following considerations: how the food is typically prepared in the U.S. and how most people obtain the food (if purchased from a store, restaurant, or homemade). Items from fast food restaurants were categorized as UPFs. Bread and rolls were presumed to be shelf-stable and purchased from grocery stores or wholesalers were classified as ultra-processed unless reported to be homemade. UPF Percentage Calculation The proportion of the diet composed of UPFs was calculated by weight rather than calories, to account for some foods that provide no contribution to energy intake, such as diet soda. The percent of diet that was ultra-processed (UPF%) was calculated as the total amount of UPFs in grams divided by the total amount of foods and beverages consumed in grams, multiplied by 100% and averaged across both recall days. If a participant only completed one dietary recall, the dietary information from the single day was used. The change in UPF consumption between visits (UPF% \(\:\varDelta\:\) ) was calculated as UPF% baseline – UPF% follow−up . Study Outcomes Glucose homeostasis was assessed using hemoglobin A1c (HbA1c) and a 2-hour oral glucose tolerance test (OGTT) glucose and insulin related measures 9 . During the OGTT, glucose and insulin were measured in plasma while fasting and at 30-, 60-, 90- and 120 minutes after glucose administration 9 , 33 . HbA1c was measured in fasting whole blood samples 9 . The glucose and insulin area under the curves (AUCs) were calculated using the trapezoidal method with the 5 time points from the OGTT 9 , 33 , 34 . Prediabetes or T2D was categorized according to the American Diabetes Association criteria. Participants were considered to have prediabetes if their HbA1c was between 5.7% and 6.4%, their fasting glucose was between 100 mg/dL and 125 mg/dL, or their 2-hour glucose was between 140 mg/dL and 199 mg/dL. Participants were considered to have T2D if their HbA1c was 6.5% or higher, their fasting glucose was 126 mg/dL or higher, or their 2-hour glucose was 200 mg/dL or higher 33 , 35 . Impaired fasting glucose (IFG) was defined as having a fasting glucose value greater than 100 mg/dL and impaired glucose tolerance (IGT) was defined as having a 2-hour glucose value greater than 140 mg/dL 36 . To assess insulin resistance and beta-cell function, the homeostatic model assessment of insulin resistance (HOMA-IR) and homeostatic model assessment of β-cell function (HOMA-β) were calculated from fasting glucose and fasting insulin values 37 . The Matsuda Index was used to estimate the insulin sensitivity of the entire body using the 5 time points from the OGTT 33 , 38 . Body composition was assessed using body mass index (BMI, kg/m 2 ) and dual-energy X-ray absorptiometry (DEXA) 9 , 39 . DEXA measures included body fat percentage, android to gynoid ratio, fat mass to height ratio (kg/m 2 ), and visceral adipose tissue (VAT) mass (g) 9 , 39 . BMI was categorized as normal weight (< 25 kg/m 2 ), overweight (25-29.9 kg/m 2 ), and obesity \(\:\ge\:\) 30 kg/m 2 ) using body weight and height measured at each visit. Covariates Demographic information including age, sex, ethnicity, and physical activity were self-reported though questionnaires at baseline and follow up 9 . Ethnicity was categorized as White, Hispanic/Latino, and Other. Exercise level at the follow-up visit was assessed using the International Physical Activity Questionnaire and categorized into High, Medium, and Low 9 , 40 . Statistical Analysis Descriptive statistics for %UPF consumption, outcomes, and covariates at both visits were calculated. Differences between categorical variables at each visit were assessed using McNamar’s test and differences between continuous variables at each visit were assessed using paired t-tests. Few participants were found to have T2D, so prediabetes and T2D were combined into one outcome group (Prediabetes/T2D) for analysis. Linear and logistic regressions were used to evaluate the effects of UPF consumption on each outcome, measured at the follow-up visit. Each model contained UPF% consumption at baseline, UPF% \(\:\varDelta\:\) , and adjusted for covariates as follows: $$\:{Y}_{outcome\:variables}=\:{\beta\:}_{0}+\:{\beta\:}_{UPF\%\:\varDelta\:}{X}_{UPF\%\varDelta\:}+\:{\beta\:}_{baseline\:UPF\%\:}{X}_{baseline\:UPF\%}$$ $$\:+covariates$$ . All models adjusted for age, sex, race and ethnicity, and exercise at the follow-up visit. Beta estimates and odds ratios were scaled by 10 units. All analyses were performed using R (version 2022.02.3 + 492; R Core Development Team). Results Descriptive Statistics Descriptive statistics for participants’ characteristics are presented in Table 1 . The proportion of the diet composed of UPFs increased, on average, from about 20% at baseline to almost 24% at follow up (p = 0.02). BMI, T2D, and IGT were not significantly different between visits, while more participants had IFG at the follow-up than at baseline (Table 1 ). Fasting glucose increased by 4.91mg/dL from the baseline to the follow-up visit (p < 0.05), while HbA1c, two-hour glucose, and glucose AUC also increased between visits, though the increase was not statistically significant (Table 2 ). Similar patterns between visits were observed where fasting insulin, two-hour insulin, HOMA- \(\:{\beta\:}\) , and HOMA-IR increased between visits, though Matsuda index significantly decreased (p < 0.05) (Table 2 ). All body composition measurements significantly increased from the baseline to the follow-up visit, except android/gynoid ratio (Table 2 ). Prediabetes/T2D and Insulin Resistance Table 3 shows the associations between UPF% \(\:\varDelta\:\) and UPF% at baseline and prediabetes/T2D, IFG, and IGT after adjusting for covariates. A 10-unit increase in UPF% \(\:\varDelta\:\) was significantly associated with 64% higher risk of having prediabetes/T2D (OR: 1.64, 95% CI: 1.15, 2.50), and with a 56% higher risk of having IGT (OR: 1.56, 95% CI: 1.42, 5.86). UPF% \(\:\varDelta\:\) was also significantly positively associated with 2-hour glucose (β = 5.72, 95% CI: 0.43–11.01) (Table 4 ). Significant positive associations were also observed between baseline UPF% and fasting insulin, 2-hour insulin, and insulin AUC, and a negative association was observed between baseline UPF% and Matsuda index (Table 4 ). Body Composition The associations between UPF% \(\:\varDelta\:\) and UPF% at baseline and body composition measurements are shown in Table 4 . There were no statistically significant associations between UPF% \(\:\varDelta\:\) or baseline UPF% and body composition. However, we observed positive but non-statistically significant associations between UPF% \(\:\varDelta\:\) and baseline UPF consumption and BMI, body fat percent, android/gynoid ratio, fat mass/height 2 and VAT mass. Table 1 Descriptive statistics for participant characteristics at baseline and follow-up visits. Baseline Follow-Up p-value 1 UPF%, mean (SD) 20.40 (12.68) 23.60 (17.73) 0.02 Age (years), mean (SD) 19.97 (1.20) 24.07 (0.75) - Sex, n (%) Female Male 43 (50.59) 42 (49.41) - - Ethnicity White Hispanic/Latino Other 30 (35.29) 49 (57.65) 6 (7.06) - - BMI Category Normal weight Overweight Obesity 13 (15.29) 35 (41.18) 37 (43.53) 11 (12.94) 32 (37.65) 42 (49.41) 0.47 Physical Activity Low Medium High N/A - 16 (18.82) 21 (24.71) 47 (55.29) 1 (1.18) - Type 2 Diabetes No Diabetes Prediabetes/T2D 61 (71.76) 24 (28.24) 53 (62.35) 32 (37.65) 0.17 IFG Normal Abnormal Missing 80 (94.12) 5 (5.88) 0 68 (80.00) 16 (18.82) 1 (1.18) 0.003 IGT Normal Abnormal Missing 69 (81.18) 16 (18.82) 0 66 (77.64) 15 (17.65) 4 (4.71) 0.82 1 p-values were calculated using a paired t-test for UPF% and McNemar’s test for BMI category, Type 2 Diabetes, IFG and IGT. *Abbreviations: UPF%: percent of the diet from ultra-processed foods; BMI: body mass index; T2D: type 2 diabetes; IFG: impaired fasting glucose; IGT: impaired glucose tolerance; SD: standard deviation Table 2 Descriptive statistics for glucose, insulin, and body composition outcomes at baseline and follow-up visits. Mean (SD) p-value 1 Baseline Follow-Up Glucose Measurements HbA1c 5.22 (0.28) 5.26 (0.52) 0.35 Fasting Glucose (mg/dL) 90.41 (7.55) 95.32 (16.61) 0.003 Two-Hour Glucose (mg/dL) 119 (26.43) 121.3 (35.04) 0.39 Glucose AUC 263.1 (44.98) 270.5 (45.11) 0.02 Insulin Measurements Fasting Insulin (µIU/mL) 7.44 (4.92) 13.27 (11.18) < 0.001 Two-Hour Insulin (µIU/mL) 57.19 (49.56) 88.33 (128.47) 0.03 Insulin AUC 152.74 (104.16) 186.5 (176.91) 0.20 HOMA- \(\:\varvec{\beta\:}\) 100.01 (63.22) 151.43 (120.26) 0.004 HOMA-IR 1.68 (1.18) 3.36 (3.73) 0.001 Matsuda Index 5.32 (3.85) 4.32 (2.83) 0.03 Body Composition BMI (kg/m 2 ) 30.09 (4.95) 31.85 (7.03) < 0.001 Body Fat (%) 35.24 (8.13) 38.3 (8.37) < 0.001 Android/Gynoid Ratio 0.99 (0.14) 1.01 (0.15) 0.29 Fat Mass/Height 2 10.62 (3.66) 12.21 (4.79) < 0.001 VAT mass (g) 506.4 (197.78) 594.3 (301.43) < 0.001 1 p-values were calculated using paired t-tests. *Abbreviations: SD: standard deviation; BMI: body mass index; VAT: visceral adipose tissue; HbA1c: Hemoglobin A1c; AUC: Area Under the Curve; HOMA- \(\:{\beta\:}\) : homeostatic model assessment of \(\:{\beta\:}\) -cell function; HOMA-IR: homeostatic model assessment for insulin resistance. Table 3 Odds ratios for the effect of change in UPF consumption and baseline UPF consumption on prediabetes/type 2 diabetes, impaired fasting glucose, and impaired glucose tolerance. OR (95% CI) UPF % \(\:\varDelta\:\) 1 UPF% at Baseline 2 Prediabetes/T2D 1.64 (1.15, 2.50) 1.34 (0.87, 2.12) IFG 1.22 (0.84, 1.78) 1.22 (0.71, 2.05) IGT 2.56 (1.42, 5.86) 1.30 (0.65, 2.66) *All outcomes were measured at the follow-up visit. Effects are scaled by 10 units. 1 Models were adjusted for age, sex, ethnicity, exercise, and UPF% at baseline. 2 Models were adjusted for age, sex, ethnicity, and UPF%Δ. **Abbreviations: UPF%: percent of diet from ultra-processed foods; UPF%Δ: change in percent of diet from ultra-processed foods between visits; T2D: type 2 diabetes; IFG: impaired fasting glucose; IGT: impaired glucose tolerance. Table 4 Effect estimates for UPF percentage change and baseline UPF consumption on body composition and glucose, insulin, and body composition measurements. \(\:\varvec{\beta\:}\) (95% CI) UPF % \(\:\varDelta\:\) 1 UPF% at Baseline 2 Glucose Measurements HbA1c 0.04 (-0.04, 0.12) -0.01 (-0.11, 0.10) Fasting Glucose 1.77 (-0.78, 4.32) 0.47 (-2.78, 3.73) Glucose After 120 minutes 5.72 (0.43, 11.01) 0.83 (-5.87, 7.53) Glucose AUC 4.89 (-1.90, 11.68) 3.36 (-5.25, 11.96) Insulin Measurements Fasting Insulin 0.63 (-0.97, 0.22) 2.09 (0.06, 4.12) Insulin After 120 mins 5.36 (-12.46, 23.18) 44.75 (22.26, 67.25) Insulin AUC 2.96 (-19.49, 25.42) 63.19 (34.84, 91.54) HOMA- \(\:\varvec{\beta\:}\) 0.17 (-16.40, 16.74) 19.14 (-2.01, 40.28) HOMA-IR 0.29 (-0.26, 0.84) 0.44 (-0.27, 1.14) Matsuda Index -0.38 (-0.79, 0.02) -0.62 (-1.11, -0.13) Body Composition BMI 0.41 (-0.66, 1.48) 0.82 (-0.54, 2.19) Body Fat Percentage 0.58 (-0.33, 1.49) 1.07 (-0.07, 2.21) Android/Gynoid Ratio 0.01 (-0.02, 0.03) 0.01 (-0.02, 0.03) Fat Mass/Height 2 0.17 (-0.52, 0.86) 0.47 (-0.39, 1.34) VAT Mass 0.13 (-4.77, 4.80) 25.26 (-34.65, 85.16) *All outcomes were measured at the follow-up visit. Effects are scaled by 10 units. 1 Models were adjusted for age, sex, ethnicity, exercise, and UPF% at baseline. 2 Models were adjusted for age, sex, ethnicity, and UPF%Δ. **Abbreviations: UPF%: percent of diet from ultra-processed foods; UPF%Δ: change in percent of diet from ultra-processed foods between visits; BMI: body mass index; VAT: visceral adipose tissue; HbA1c: Hemoglobin A1c; AUC: Area Under the Curve; HOMA- \(\:{\beta\:}\) : homeostatic model assessment of \(\:{\beta\:}\) -cell function; HOMA-IR: homeostatic model assessment for insulin resistance. Discussion In this novel longitudinal analysis, we found that increasing UPF consumption over a four-year period increased the risk for prediabetes and IGT in young adults. Higher UPF consumption was associated with significant increases in some markers of insulin sensitivity including fasting insulin, 2-hour insulin, and insulin AUC. Positive but non-significant associations with most measures of adiposity were also observed. Increases in UPF consumption between study visits was also associated with decreasing Matsuda index, a measure of insulin sensitivity that describes insulin secretion relative to blood glucose 38 . These findings suggest that UPF consumption is associated with increased risk for insulin resistance. Importantly, since early prevention and T2D treatment among young adults can be highly effective, our results highlight the adverse impact of UPF consumption on T2D development and emphasize the importance on dietary habits for young adults 28 . Previous studies have investigated associations between UPF consumption and T2D, though none have included detailed glucose and insulin measurements to explore possible mechanisms of T2D development or the changes in glucose homeostasis that could lead to T2D 27 , 41 , 42 . In addition to positive associations between UPF consumption and IGT and prediabetes, we also found associations between UPF consumption and insulin resistance by multiple measures: lower Matsuda indices, higher insulin concentrations across the OGTT, and positive, but non-significant, association with HOMA-IR. Insulin resistance and beta-cell dysfunction are important physiological characteristics of T2D and can be influenced by diet. Some nutritional components of many UPFs, including saturated fat, free fatty acids, and added sugars, are known to affect beta-cell function. If consumed in excess, these nutrients could exhaust beta-cells, inhibiting their function and further contributing to insulin resistance and eventually T2D 43 . Though we did not observe any inverse associations between UPF consumption and HOMA-β, this does not exclude beta-cell exhaustion as a possible mechanism underlying the relationship between UPF consumption and T2D; relationships between HOMA-β and risk for T2D are inconsistent across different populations and among populations at different stages of T2D progression 44 , 45 . It is important to continue to explore the impact of UPF consumption and its underlying role on developing T2D. Growing proportions of the diet are composed of UPFs, which are usually high in added sugars, saturated fats, or other nutrients, leading to lower diet quality and increasing prevalence of diet-associated chronic diseases 15 – 18 . Existing studies on UPF consumption have primarily focused on middle-aged or older adults, yet the early onset of T2D among young adults is rising, suggesting the need for evaluation and interventions targeted at youth 1 . Studies in older adults have showed that higher UPF consumption is associated with an increased risk for T2D, which is consistent with our findings 46 – 50 . However, evidence for associations between overweight or obesity, both risk factors for T2D, and UPF consumption is inconsistent across different age groups. While many studies have reported a positive association between UPF consumption and overweight and/or obesity among adults, others did not observe the same pattern in children 21 , 22 , 41 , 50 , 51 . We observed mostly positive, but non-significant, relationships between UPF consumption and obesity-related body compositive measurements, which is consistent with previous studies in adults 23 – 25 . These studies suggested that reducing UPF consumption may also benefit adults by preventing excess weight gain 23 – 25 . However, the lack of statistical significance in our findings may be due to the limited sample size, suggesting larger sample sizes are needed for future studies of the impact of UPF consumption on young adults. Many UPFs are high in salt, fat, and sugar, which could independently contribute to metabolic diseases, and thus are especially relevant targets of public health interventions 52 – 54 . High salt intake may be a contributor to obesity and diabetes 55 – 57 , and dietary fat, especially trans fatty acids and saturated fats, is positively associated with T2D and obesity risks due to its effects on insulin sensitivity 57 – 60 . Consumption of soft drinks and foods that contain high amounts of added sugar were also found to increase risk for T2D and obesity, and limiting added sugar intake and UPFs may prevent chronic disease in children and adolescents 16 , 61 , 62 . Previous work and the findings from our present study suggest that the nutrients common to UPFs increase the risk for obesity, which may be a mechanism by which UPFs increase the risk for T2D. This study has many strengths. Firstly, gold-standard outcome measurements were obtained using OGTT and DEXA at both time points 9 . Secondly, our study focuses on young adults: an age group not often included in previous work. Young adults in their late teens and twenties have only recently reached physically mature and are undergoing significant lifestyle changes that may affect their risk for obesity and T2D. Thirdly, this study is one of the few longitudinal studies to examine the relationship between UPFs and risk for prediabetes, insulin resistance, and obesity, where diet and each outcome was assessed at each time point. This study design allowed us to evaluate the changes in UPF consumption over several years to follow up and assess the resulting impacts on glucose homeostasis and insulin sensitivity. However, this study also has some limitations. Our relatively small sample size may have limited our statistical power to detect associations between UPF consumption and some outcomes. Despite this, we did have enough power to consistently detect associations between UPF% consumption and prediabetes, IGT, and markers of insulin resistance. Additionally, dietary recalls may be subject to recall bias and may not represent long-term eating habits, though administration of multiple recalls improves estimates of nutrient intake and the variety of foods consumed 9 . We also used two different dietary recall systems, NDSR and ASA24, for the baseline and follow-up visits, which use different databases for reported foods. However, both systems provided a similar level of detail about food processing and sources, and we do not expect that one or the other would systematically encourage classification into higher NOVA processing categories 62 , 63 . While it is possible that some misclassification of UPFs occurred, though we minimized this by preforming the classification using two independent researchers, and we would expect any misclassification to be non-differential. Findings from this study suggest that reducing UPF consumption may reduce the risk for prediabetes and T2D in youth. Young adults may also benefit from limiting foods that contain high amounts of salt, fat, and sugar as they all potentially contribute to obesity, which also increases the risk for T2D 4 , 5 . Metabolic diseases such as T2D and obesity are significant public health problems and are becoming more prevalent among young adults 1 ,64 . Our study also shows that UPF consumption is associated with insulin resistance, a risk factor for T2D and a condition not commonly assessed in previous studies of UPFs and T2D risk. Because more than half of the total daily energy in consumption in the US is from UPFs, this modifiable risk factor is a possible target for both individual and public health interventions in preventing metabolic diseases. Future studies may incorporate additional methods of diet assessment and larger sample sizes to improve our understanding of the eating habits of young adults and the mechanisms underlying associations between UPF consumption and metabolic diseases. Conclusions This prospective study found that UPF consumption is positively associated with increased risk for prediabetes among young adults. Increasing UPF consumption was also associated with impaired glucose tolerance and insulin resistance, known risk factors for the future development of T2D. This study evaluated a unique population of the youth with detailed longitudinal measurements of diet and glucose homeostasis. These findings indicate that limiting the consumption of UPFs may be an important strategy for T2D prevention among young adults. Declarations Acknowledgements: Y.L., E.C. and L.C designed research; E.C., W.B.P. and S.R. conducted research; Y.L. and E.C. performed statistical analysis; Y.L. and E.L. wrote the paper; Y.L. had primary responsibility for final content; W.B.P., Z.C., F.G., M.I.G., T.L.A., J.A.G., D.V.C, N.S. and L.C. reviewed and edited the paper. All authors have read and approved the final manuscript. Data Availability: Data described in the manuscript, code book, and analytic code will be made available upon request. The data are not publicly available to protect participants’ identifiable information. Funding: The results reported here in correspond to specific aims of grant R01ES029944 from the National Institute of Environmental Health Science (NIEHS). Funding for the MetaAir study came from the Southern California Children’s Environmental Health Center grants funded by NIEHS (5P01ES022845-03, P30ES007048, 5P01ES011627), the United States Environmental Protection Agency (RD83544101), and the Hastings Foundation. Additional funding from NIEHS supported Dr. Chatzi (R01ES029944, R01ES030364, U01HG013288, and P30ES007048), and Dr. Costello (T32ES013678 and U01HG013288). Other support from the European Union supported Dr. Chatzi (The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project: 874583), Dr. Alderate (NIEHS: R01ES035035 and R01ES035056 and National Institute on Minority Health and Health Disparities: P50MD017344), and Dr. Stratakis (Horizon Europe Research and Innovation Program under the Marie Skłodowska-Curie Actions Postdoctoral Fellowships: 101059245). Declaration of Generative AI and AI-assisted technologies in the writing process: The authors did not use any AI tool during the preparation of this work. References Divers J, Mayer-Davis EJ, Lawrence JM, Isom S, Dabelea D, Dolan L, et al. Trends in Incidence of Type 1 and Type 2 Diabetes Among Youths — Selected Counties and Indian Reservations, United States, 2002–2015. MMWR Morb Mortal Wkly Rep. 2020;69(6):161–5. 10.15585/mmwr.mm6906a3 . Fuster-Parra P, Yañez AM, López-González A, Aguiló A, Bennasar-Veny M. 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Lavigne-Robichaud M, Moubarac JC, Lantagne-Lopez S, Johnson-Down L, Batal M, Laouan Sidi EA, et al. Diet quality indices in relation to metabolic syndrome in an Indigenous Cree (Eeyouch) population in northern Québec, Canada. Public Health Nutr. 2018;21(1):172–80. 10.1017/S136898001700115X . Canhada SL, Vigo Á, Luft VC, Levy RB, Alvim Matos SM, del Carmen Molina M, et al. Ultra-Processed Food Consumption and Increased Risk of Metabolic Syndrome in Adults: The ELSA-Brasil. Diabetes Care. 2023;46(2):369–76. 10.2337/dc22-1505 . Llavero-Valero M, Escalada-San Martín J, Martínez-González MA, Basterra-Gortari FJ, de la Fuente-Arrillaga C, Bes-Rastrollo M. Ultra-processed foods and type-2 diabetes risk in the SUN project: A prospective cohort study. Clin Nutr. 2021;40(5):2817–24. 10.1016/j.clnu.2021.03.039 . Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al. 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Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–9. 10.1007/BF00280883 . Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999;22(9):1462–70. 10.2337/diacare.22.9.1462 . Lorente Ramos RM, Azpeitia Armán J, Arévalo Galeano N, Muñoz Hernández A, García Gómez JM. J Gredilla Molinero. Dual energy X-ray absorptimetry: fundamentals, methodology, and clinical applications. Radiologia. 2012(54(5)):410–23. doi: 10.1016/j.rx.2011.09.023 Craig CL, Marshall AL, Sj??Str??M M, Bauman AE, Booth ML, Ainsworth BE, et al. International Physical Activity Questionnaire: 12-Country Reliability and Validity. Med Sci Sports Exerc. 2003;35(8):1381–95. 10.1249/01.MSS.0000078924.61453.FB . Dicken SJ, Batterham RL. The Role of Diet Quality in Mediating the Association between Ultra-Processed Food Intake, Obesity and Health-Related Outcomes: A Review of Prospective Cohort Studies. Nutrients. 2021;14(1):23. 10.3390/nu14010023 . Costa CS, Del-Ponte B, Assunção MCF, Santos IS. Consumption of ultra-processed foods and body fat during childhood and adolescence: a systematic review. Public Health Nutr. 2018;21(1):148–59. 10.1017/S1368980017001331 . Cerf ME. Beta Cell Dysfunction and Insulin Resistance. Front Endocrinol. 2013;4. 10.3389/fendo.2013.00037 . Khalili D, Khayamzadeh M, Kohansal K, Ahanchi NS, Hasheminia M, Hadaegh F, et al. Are HOMA-IR and HOMA-B good predictors for diabetes and pre-diabetes subtypes? BMC Endocr Disord. 2023;23(1):39. 10.1186/s12902-023-01291-9 . Tabák AG, Jokela M, Akbaraly TN, Brunner EJ, Kivimäki M, Witte DR. Trajectories of glycaemia, insulin sensitivity, and insulin secretion before diagnosis of type 2 diabetes: an analysis from the Whitehall II study. Lancet. 2009;373(9682):2215–21. 10.1016/S0140-6736(09)60619-X . Almarshad MI, Algonaiman R, Alharbi HF, Almujaydil MS, Barakat H. Relationship between Ultra-Processed Food Consumption and Risk of Diabetes Mellitus: A Mini-Review. Nutrients. 2022;14(12):2366. 10.3390/nu14122366 . Moradi S, ali Hojjati Kermani M, Bagheri R, Mohammadi H, Jayedi A, Lane MM, et al. Ultra-Processed Food Consumption and Adult Diabetes Risk: A Systematic Review and Dose-Response Meta-Analysis. Nutrients. 2021;13(12):4410. 10.3390/nu13124410 . Delpino FM, Figueiredo LM, Bielemann RM, da Silva BGC, dos Santos FS, Mintem GC, et al. Ultra-processed food and risk of type 2 diabetes: a systematic review and meta-analysis of longitudinal studies. Int J Epidemiol. 2022;51(4):1120–41. 10.1093/ije/dyab247 . Popkin BM, Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. Obes Rev. 2022;23(1). 10.1111/obr.13366 . Elizabeth L, Machado P, Zinöcker M, Baker P, Lawrence M. Ultra-Processed Foods and Health Outcomes: A Narrative Review. Nutrients. 2020;12(7):1955. 10.3390/nu12071955 . Pan F, Wang Z, Wang H, Zhang J, Su C, Jia X, et al. Association between Ultra-Processed Food Consumption and Metabolic Syndrome among Adults in China—Results from the China Health and Nutrition Survey. Nutrients. 2023;15(3):752. 10.3390/nu15030752 . Poti JM, Mendez MA, Ng SW, Popkin BM. Is the degree of food processing and convenience linked with the nutritional quality of foods purchased by US households? Am J Clin Nutr. 2015;101(6):1251–62. 10.3945/ajcn.114.100925 . Pulgaron ER, Delamater AM. Obesity and type 2 diabetes in children: epidemiology and treatment. Curr Diab Rep. 2014;14(8):508. 10.1007/s11892-014-0508-y . Maggio CA, Pi-Sunyer FX. Obesity and type 2 diabetes. Endocrinol Metab Clin North Am. 2003;32(4):805–22. 10.1016/S0889-8529(03)00071-9 . Lanaspa MA, Kuwabara M, Andres-Hernando A, Li N, Cicerchi C, Jensen T, et al. High salt intake causes leptin resistance and obesity in mice by stimulating endogenous fructose production and metabolism. Proc Natl Acad Sci U S A. 2018;115(12):3138–43. 10.1073/pnas.1713837115 . Ma Y, He FJ, MacGregor GA. High salt intake: independent risk factor for obesity? Hypertens Dallas Tex 1979. 2015;66(4):843–9. 10.1161/HYPERTENSIONAHA.115.05948 . Rouhani P, Mirzaei S, Asadi A, Akhlaghi M, Saneei P. Nutrient patterns in relation to metabolic health status in overweight and obese adolescents. Sci Rep. 2023;13(1):119. 10.1038/s41598-023-27510-w . Risérus U, Willett WC, Hu FB. Dietary fats and prevention of type 2 diabetes. Prog Lipid Res. 2009;48(1):44–51. 10.1016/j.plipres.2008.10.002 . Rice Bradley BH. Dietary Fat and Risk for Type 2 Diabetes: a Review of Recent Research. Curr Nutr Rep. 2018;7(4):214–26. 10.1007/s13668-018-0244-z . Sami W, Ansari T, Butt NS, Hamid MRA. Effect of diet on type 2 diabetes mellitus: A review. Int J Health Sci. 2017;11(2):65–71. Rippe JM, Angelopoulos TJ. Relationship between Added Sugars Consumption and Chronic Disease Risk Factors: Current Understanding. Nutrients. 2016;8(11):697. 10.3390/nu8110697 . Maitre L, de Bont J, Casas M, Robinson O, Aasvang GM, Agier L, et al. Human Early Life Exposome (HELIX) study: a European population-based exposome cohort. BMJ Open. 2018;8(9):e021311. 10.1136/bmjopen-2017-021311 . Smith-Warner SA, Spiegelman D, Ritz J, Albanes D, Beeson WL, Bernstein L, et al. Methods for Pooling Results of Epidemiologic Studies. Am J Epidemiol. 2006;163(11):1053–64. 10.1093/aje/kwj127 . Additional Declarations No competing interests reported. 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In the United States (US), the prevalence of diagnosed T2D is estimated to be about 48 per 100,000 in youth under 20 years of age\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. T2D is also a significant global public health concern because it can affect individuals\u0026rsquo; quality of life, lead to many comorbidities, and increase mortality risk\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The early onset of T2D among young adults can lead to more long-term health issues compared to onset of T2D in later adulthood\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Obesity greatly increases the risk for prediabetes and T2D, and poor diet and other lifestyle factors can be risk factors for all three conditions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Since prediabetes, T2D, and obesity are closely related to each other and share similar risk factors, the assessment of modifiable risk factors like diet is crucial for prevention and treatment of these conditions.\u003c/p\u003e \u003cp\u003eIn the US, more than half of total dietary energy consumption is composed of ultra-processed foods (UPFs)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. UPFs are food items that go through multiple industrial processes before people purchase or eat them\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Examples of common UPFs include soft drinks, packaged snacks, margarine, and sausages\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Most UPFs are calorie-dense and high in sugar, salt, and unhealthy fats, while low in protein, vitamins, and minerals\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Studies have demonstrated that higher consumption of UPFs results in poor nutritional diet quality and increased risk for the development of chronic diseases, including T2D and hypertension\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. It is important to limit UPF consumption in childhood and adolescence due to the high content of added sugar and saturated fats in UPFs and their possible contribution to weight gain, T2D, cardiovascular disease, and hypertension\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThough many risk factors for metabolic disease first appear in early life, most research studies have focused on the effects of UPFs on metabolic disease in middle-aged and older adults\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Many of these studies show that diets with a higher proportion of UPFs or increasing consumption of UPFs is associated with a higher risk of T2D and obesity among adults\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In addition, most previous studies examining UPF consumption and metabolic disease have been conducted in Brazil and primarily used cross-sectional analyses, with only a few using a longitudinal study design\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, few studies have assessed the associations between UPFs and T2D or obesity in young people, and those that have are cross-sectional and report mixed results\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Some studies have shown that limiting the consumption of UPFs can reduce T2D and obesity risk among children and young adults, while others found no association between UPF consumption and obesity or overweight\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Because of the limited studies on UPF consumption among young adults, and the importance of early lifestyle changes in preventing T2D among high risk populations, more research is needed to understand the relationship between UPF consumption and risk for T2D and obesity in young adults\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe purpose of this study is to assess the longitudinal associations between UPF consumption and prediabetes and obesity in young adults, using glucose and insulin measurements, body composition, and diet assessment over four years of follow-up. We hypothesized that increases in UPF consumption would be associated with a higher risk of altered glucose homeostasis, insulin resistance, obesity, and prediabetes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCohort\u003c/h2\u003e \u003cp\u003eBetween 2014\u0026ndash;2018, 155 young adults aged 17\u0026ndash;22 who had previously participated in the Children\u0026rsquo;s Health Study were invited to enroll in the Meta-AIR study\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. To be eligible, participants had a history of overweight or obesity in early adolescence, were not diagnosed with either type 1 or type 2 diabetes, were not taking medications that influence glucose metabolism, and had no other significant medical diagnosis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Of these, 85 returned for a follow-up visit between 2020\u0026ndash;2022 (the MetaCHEM study, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This study was approved by the Institutional Review Board at the University of Southern California and written informed consent or assent were obtained from participants and their guardians.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDietary Assessment and UPF Classification\u003c/h3\u003e\n\u003cp\u003eParticipants completed two non-consecutive 24-hour dietary recalls on one weekday and one weekend day at each visit\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Trained interviewers used the Nutritional Data System for Research (NDSR) software version 2014 to complete the baseline recalls, while participants used the Automated Self-Administered 24-hr Dietary Assessment Tool (ASA24) version 2018 to conduct the recalls at the follow-up visit\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. At baseline, 10.3% (n\u0026thinsp;=\u0026thinsp;16) of participants completed only one recall, while at follow up 10.2% (n\u0026thinsp;=\u0026thinsp;9) of participant completed only one recall.\u003c/p\u003e \u003cp\u003eIn this study, a total of 1,167 unique food items were reported at the baseline visit and a total of 807 unique food items were reported the at the follow-up visit. Some food items reported at the follow-up visit contained mixed dishes with multiple ingredients. When possible, these foods were disaggregated into individual ingredients using the 2017\u0026ndash;2018 Food and Nutrient Database for Dietary Studies (FNDDS) Ingredients database and matched to the food codes from the FNDDS Foods and Beverages database that were provided by ASA24\u003csup\u003e9,31\u003c/sup\u003e. Each ingredient or food was classified as ultra-processed or not by two independent reviewers (YL, EC). Foods were classified according to the NOVA group definitions of 1) unprocessed and minimally processed foods, 2) processed culinary ingredients, 3) processed foods, and 4) ultra-processed foods (UPFs)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Any disagreements on UPF classification were resolved through discussion.\u003c/p\u003e \u003cp\u003eBriefly, unprocessed and minimally processed foods in NOVA group 1 are edible parts of plants or animals and foods that have been through basic processing such as drying, griding and freezing (e.g., fruits and vegetables, eggs, meat and seafood, flour, spices)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Processed culinary ingredients in NOVA group 2 mainly composed of vegetable oils, butter, salt, sugar, and honey for the main purpose of seasoning and cooking\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Processed foods in NOVA group 3 contain food products that are made by adding items in group 2 to group 1 for the purpose of preservation (e.g., canned vegetables, cured or smoked meat and fish, freshly made bread)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. UPFs in NOVA group 4 are made with series of industrial techniques that cannot be replicated at home and may involve molding or pre-frying, the addition of colors and flavors, and often includes added sugars, salt, oils, and fats\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This group also includes most branded and packaged foods, pre-prepared ready-to-eat products, and instant foods\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, enriched food items, like milk or flour with added vitamins or minerals, are not included in this group. Examples of UPFs are soft drinks, candies, cereal, ice-cream, mass-produced cookies or pastries, margarines, packaged and shelf-stable spreads, milk drinks, flavored yogurts, pizza, and sausages.\u003c/p\u003e \u003cp\u003eUPF classification was made with the following considerations: how the food is typically prepared in the U.S. and how most people obtain the food (if purchased from a store, restaurant, or homemade). Items from fast food restaurants were categorized as UPFs. Bread and rolls were presumed to be shelf-stable and purchased from grocery stores or wholesalers were classified as ultra-processed unless reported to be homemade.\u003c/p\u003e\n\u003ch3\u003eUPF Percentage Calculation\u003c/h3\u003e\n\u003cp\u003eThe proportion of the diet composed of UPFs was calculated by weight rather than calories, to account for some foods that provide no contribution to energy intake, such as diet soda. The percent of diet that was ultra-processed (UPF%) was calculated as the total amount of UPFs in grams divided by the total amount of foods and beverages consumed in grams, multiplied by 100% and averaged across both recall days. If a participant only completed one dietary recall, the dietary information from the single day was used. The change in UPF consumption between visits (UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e) was calculated as UPF%\u003csub\u003ebaseline\u003c/sub\u003e \u0026ndash; UPF%\u003csub\u003efollow\u0026minus;up\u003c/sub\u003e.\u003c/p\u003e\n\u003ch3\u003eStudy Outcomes\u003c/h3\u003e\n\u003cp\u003eGlucose homeostasis was assessed using hemoglobin A1c (HbA1c) and a 2-hour oral glucose tolerance test (OGTT) glucose and insulin related measures\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. During the OGTT, glucose and insulin were measured in plasma while fasting and at 30-, 60-, 90- and 120 minutes after glucose administration\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. HbA1c was measured in fasting whole blood samples\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The glucose and insulin area under the curves (AUCs) were calculated using the trapezoidal method with the 5 time points from the OGTT\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrediabetes or T2D was categorized according to the American Diabetes Association criteria. Participants were considered to have prediabetes if their HbA1c was between 5.7% and 6.4%, their fasting glucose was between 100 mg/dL and 125 mg/dL, or their 2-hour glucose was between 140 mg/dL and 199 mg/dL. Participants were considered to have T2D if their HbA1c was 6.5% or higher, their fasting glucose was 126 mg/dL or higher, or their 2-hour glucose was 200 mg/dL or higher\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Impaired fasting glucose (IFG) was defined as having a fasting glucose value greater than 100 mg/dL and impaired glucose tolerance (IGT) was defined as having a 2-hour glucose value greater than 140 mg/dL\u003csup\u003e36\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo assess insulin resistance and beta-cell function, the homeostatic model assessment of insulin resistance (HOMA-IR) and homeostatic model assessment of β-cell function (HOMA-β) were calculated from fasting glucose and fasting insulin values\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The Matsuda Index was used to estimate the insulin sensitivity of the entire body using the 5 time points from the OGTT\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBody composition was assessed using body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) and dual-energy X-ray absorptiometry (DEXA)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. DEXA measures included body fat percentage, android to gynoid ratio, fat mass to height ratio (kg/m\u003csup\u003e2\u003c/sup\u003e), and visceral adipose tissue (VAT) mass (g)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. BMI was categorized as normal weight (\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e30 kg/m\u003csup\u003e2\u003c/sup\u003e) using body weight and height measured at each visit.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDemographic information including age, sex, ethnicity, and physical activity were self-reported though questionnaires at baseline and follow up\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Ethnicity was categorized as White, Hispanic/Latino, and Other. Exercise level at the follow-up visit was assessed using the International Physical Activity Questionnaire and categorized into High, Medium, and Low\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics for %UPF consumption, outcomes, and covariates at both visits were calculated. Differences between categorical variables at each visit were assessed using McNamar\u0026rsquo;s test and differences between continuous variables at each visit were assessed using paired t-tests. Few participants were found to have T2D, so prediabetes and T2D were combined into one outcome group (Prediabetes/T2D) for analysis.\u003c/p\u003e \u003cp\u003eLinear and logistic regressions were used to evaluate the effects of UPF consumption on each outcome, measured at the follow-up visit. Each model contained UPF% consumption at baseline, UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e, and adjusted for covariates as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{outcome\\:variables}=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{UPF\\%\\:\\varDelta\\:}{X}_{UPF\\%\\varDelta\\:}+\\:{\\beta\\:}_{baseline\\:UPF\\%\\:}{X}_{baseline\\:UPF\\%}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:+covariates$$\u003c/div\u003e\u003c/div\u003e.\u003c/p\u003e \u003cp\u003eAll models adjusted for age, sex, race and ethnicity, and exercise at the follow-up visit. Beta estimates and odds ratios were scaled by 10 units. All analyses were performed using R (version 2022.02.3\u0026thinsp;+\u0026thinsp;492; R Core Development Team).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics for participants\u0026rsquo; characteristics are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The proportion of the diet composed of UPFs increased, on average, from about 20% at baseline to almost 24% at follow up (p\u0026thinsp;=\u0026thinsp;0.02). BMI, T2D, and IGT were not significantly different between visits, while more participants had IFG at the follow-up than at baseline (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Fasting glucose increased by 4.91mg/dL from the baseline to the follow-up visit (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while HbA1c, two-hour glucose, and glucose AUC also increased between visits, though the increase was not statistically significant (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Similar patterns between visits were observed where fasting insulin, two-hour insulin, HOMA-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e, and HOMA-IR increased between visits, though Matsuda index significantly decreased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). All body composition measurements significantly increased from the baseline to the follow-up visit, except android/gynoid ratio (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003ePrediabetes/T2D and Insulin Resistance\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the associations between UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e and UPF% at baseline and prediabetes/T2D, IFG, and IGT after adjusting for covariates. A 10-unit increase in UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e was significantly associated with 64% higher risk of having prediabetes/T2D (OR: 1.64, 95% CI: 1.15, 2.50), and with a 56% higher risk of having IGT (OR: 1.56, 95% CI: 1.42, 5.86). UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e was also significantly positively associated with 2-hour glucose (\u0026beta;\u0026thinsp;=\u0026thinsp;5.72, 95% CI: 0.43\u0026ndash;11.01) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Significant positive associations were also observed between baseline UPF% and fasting insulin, 2-hour insulin, and insulin AUC, and a negative association was observed between baseline UPF% and Matsuda index (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eBody Composition\u003c/h2\u003e\n \u003cp\u003eThe associations between UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e and UPF% at baseline and body composition measurements are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. There were no statistically significant associations between UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e or baseline UPF% and body composition. However, we observed positive but non-statistically significant associations between UPF%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e and baseline UPF consumption and BMI, body fat percent, android/gynoid ratio, fat mass/height\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and VAT mass.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics for participant characteristics at baseline and follow-up visits.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 39.6948%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFollow-Up\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eUPF%, mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e20.40 (12.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.60 (17.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e19.97 (1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.07 (0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e43 (50.59)\u003c/p\u003e\n \u003cp\u003e42 (49.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eEthnicity\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e30 (35.29)\u003c/p\u003e\n \u003cp\u003e49 (57.65)\u003c/p\u003e\n \u003cp\u003e6 (7.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eBMI Category\u003c/p\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e13 (15.29)\u003c/p\u003e\n \u003cp\u003e35 (41.18)\u003c/p\u003e\n \u003cp\u003e37 (43.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e11 (12.94)\u003c/p\u003e\n \u003cp\u003e32 (37.65)\u003c/p\u003e\n \u003cp\u003e42 (49.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003ePhysical Activity\u003c/p\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e16 (18.82)\u003c/p\u003e\n \u003cp\u003e21 (24.71)\u003c/p\u003e\n \u003cp\u003e47 (55.29)\u003c/p\u003e\n \u003cp\u003e1 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eType 2 Diabetes\u003c/p\u003e\n \u003cp\u003eNo Diabetes\u003c/p\u003e\n \u003cp\u003ePrediabetes/T2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e61 (71.76)\u003c/p\u003e\n \u003cp\u003e24 (28.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e53 (62.35)\u003c/p\u003e\n \u003cp\u003e32 (37.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eIFG\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e80 (94.12)\u003c/p\u003e\n \u003cp\u003e5 (5.88)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e68 (80.00)\u003c/p\u003e\n \u003cp\u003e16 (18.82)\u003c/p\u003e\n \u003cp\u003e1 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 39.6948%;\"\u003e\n \u003cp\u003eIGT\u003c/p\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003eAbnormal\u003c/p\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 22.9006%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e69 (81.18)\u003c/p\u003e\n \u003cp\u003e16 (18.82)\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e66 (77.64)\u003c/p\u003e\n \u003cp\u003e15 (17.65)\u003c/p\u003e\n \u003cp\u003e4 (4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026nbsp;\u003c/sup\u003ep-values were calculated using a paired t-test for UPF% and McNemar\u0026rsquo;s test for BMI category, Type 2 Diabetes, IFG and IGT.\u003c/p\u003e\n \u003cp\u003e*Abbreviations: UPF%: percent of the diet from ultra-processed foods; BMI: body mass index; T2D: type 2 diabetes; IFG: impaired fasting glucose; IGT: impaired glucose tolerance; SD: standard deviation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics for glucose, insulin, and body composition outcomes at baseline and follow-up visits.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollow-Up\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eGlucose Measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.22 (0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.26 (0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting Glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.41 (7.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.32 (16.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo-Hour Glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (26.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121.3 (35.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263.1 (44.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e270.5 (45.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eInsulin Measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting Insulin (\u0026micro;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.44 (4.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.27 (11.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo-Hour Insulin (\u0026micro;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.19 (49.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.33 (128.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.74 (104.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186.5 (176.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.01 (63.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151.43 (120.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36 (3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMatsuda Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.32 (3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBody Composition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.09 (4.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.85 (7.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.24 (8.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.3 (8.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndroid/Gynoid Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01 (0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFat Mass/Height\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.62 (3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.21 (4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAT mass (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e506.4 (197.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e594.3 (301.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026nbsp;\u003c/sup\u003ep-values were calculated using paired t-tests.\u003c/p\u003e\n \u003cp\u003e*Abbreviations: SD: standard deviation; BMI: body mass index; VAT: visceral adipose tissue; HbA1c: Hemoglobin A1c; AUC: Area Under the Curve; HOMA-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e: homeostatic model assessment of\u0026nbsp;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e-cell function; HOMA-IR: homeostatic model assessment for insulin resistance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOdds ratios for the effect of change in UPF consumption and baseline UPF consumption on prediabetes/type 2 diabetes, impaired fasting glucose, and impaired glucose tolerance.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPF\u003c/strong\u003e%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPF% at Baseline\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrediabetes/T2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64 (1.15, 2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34 (0.87, 2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.84, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22 (0.71, 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56 (1.42, 5.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30 (0.65, 2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*All outcomes were measured at the follow-up visit. Effects are scaled by 10 units.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026nbsp;\u003c/sup\u003eModels were adjusted for age, sex, ethnicity, exercise, and UPF% at baseline.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026nbsp;\u003c/sup\u003eModels were adjusted for age, sex, ethnicity, and UPF%\u0026Delta;.\u003c/p\u003e\n \u003cp\u003e**Abbreviations: UPF%: percent of diet from ultra-processed foods; UPF%\u0026Delta;: change in percent of diet from ultra-processed foods between visits; T2D: type 2 diabetes; IFG: impaired fasting glucose; IGT: impaired glucose tolerance.\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect estimates for UPF percentage change and baseline UPF consumption on body composition and glucose, insulin, and body composition measurements.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPF\u003c/strong\u003e%\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varDelta\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUPF% at Baseline\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose Measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (-0.04, 0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.11, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77 (-0.78, 4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (-2.78, 3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose After 120 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.72 (0.43, 11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (-5.87, 7.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.89 (-1.90, 11.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36 (-5.25, 11.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin Measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting Insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63 (-0.97, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.09 (0.06, 4.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin After 120 mins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.36 (-12.46, 23.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.75 (22.26, 67.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin AUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96 (-19.49, 25.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.19 (34.84, 91.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (-16.40, 16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.14 (-2.01, 40.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (-0.26, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (-0.27, 1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMatsuda Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38 (-0.79, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.62 (-1.11, -0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Composition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41 (-0.66, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82 (-0.54, 2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Fat Percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 (-0.33, 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07 (-0.07, 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAndroid/Gynoid Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFat Mass/Height\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (-0.52, 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47 (-0.39, 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAT Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (-4.77, 4.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.26 (-34.65, 85.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e*All outcomes were measured at the follow-up visit. Effects are scaled by 10 units.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026nbsp;\u003c/sup\u003eModels were adjusted for age, sex, ethnicity, exercise, and UPF% at baseline.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026nbsp;\u003c/sup\u003eModels were adjusted for age, sex, ethnicity, and UPF%\u0026Delta;.\u003c/p\u003e\n \u003cp\u003e**Abbreviations: UPF%: percent of diet from ultra-processed foods; UPF%\u0026Delta;: change in percent of diet from ultra-processed foods between visits; BMI: body mass index; VAT: visceral adipose tissue; HbA1c: Hemoglobin A1c; AUC: Area Under the Curve; HOMA-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e: homeostatic model assessment of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e-cell function; HOMA-IR: homeostatic model assessment for insulin resistance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this novel longitudinal analysis, we found that increasing UPF consumption over a four-year period increased the risk for prediabetes and IGT in young adults. Higher UPF consumption was associated with significant increases in some markers of insulin sensitivity including fasting insulin, 2-hour insulin, and insulin AUC. Positive but non-significant associations with most measures of adiposity were also observed. Increases in UPF consumption between study visits was also associated with decreasing Matsuda index, a measure of insulin sensitivity that describes insulin secretion relative to blood glucose\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These findings suggest that UPF consumption is associated with increased risk for insulin resistance. Importantly, since early prevention and T2D treatment among young adults can be highly effective, our results highlight the adverse impact of UPF consumption on T2D development and emphasize the importance on dietary habits for young adults\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have investigated associations between UPF consumption and T2D, though none have included detailed glucose and insulin measurements to explore possible mechanisms of T2D development or the changes in glucose homeostasis that could lead to T2D\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In addition to positive associations between UPF consumption and IGT and prediabetes, we also found associations between UPF consumption and insulin resistance by multiple measures: lower Matsuda indices, higher insulin concentrations across the OGTT, and positive, but non-significant, association with HOMA-IR. Insulin resistance and beta-cell dysfunction are important physiological characteristics of T2D and can be influenced by diet. Some nutritional components of many UPFs, including saturated fat, free fatty acids, and added sugars, are known to affect beta-cell function. If consumed in excess, these nutrients could exhaust beta-cells, inhibiting their function and further contributing to insulin resistance and eventually T2D\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Though we did not observe any inverse associations between UPF consumption and HOMA-β, this does not exclude beta-cell exhaustion as a possible mechanism underlying the relationship between UPF consumption and T2D; relationships between HOMA-β and risk for T2D are inconsistent across different populations and among populations at different stages of T2D progression\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. It is important to continue to explore the impact of UPF consumption and its underlying role on developing T2D.\u003c/p\u003e \u003cp\u003eGrowing proportions of the diet are composed of UPFs, which are usually high in added sugars, saturated fats, or other nutrients, leading to lower diet quality and increasing prevalence of diet-associated chronic diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Existing studies on UPF consumption have primarily focused on middle-aged or older adults, yet the early onset of T2D among young adults is rising, suggesting the need for evaluation and interventions targeted at youth\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Studies in older adults have showed that higher UPF consumption is associated with an increased risk for T2D, which is consistent with our findings\u003csup\u003e\u003cspan additionalcitationids=\"CR47 CR48 CR49\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. However, evidence for associations between overweight or obesity, both risk factors for T2D, and UPF consumption is inconsistent across different age groups. While many studies have reported a positive association between UPF consumption and overweight and/or obesity among adults, others did not observe the same pattern in children\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. We observed mostly positive, but non-significant, relationships between UPF consumption and obesity-related body compositive measurements, which is consistent with previous studies in adults\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These studies suggested that reducing UPF consumption may also benefit adults by preventing excess weight gain\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, the lack of statistical significance in our findings may be due to the limited sample size, suggesting larger sample sizes are needed for future studies of the impact of UPF consumption on young adults.\u003c/p\u003e \u003cp\u003eMany UPFs are high in salt, fat, and sugar, which could independently contribute to metabolic diseases, and thus are especially relevant targets of public health interventions\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. High salt intake may be a contributor to obesity and diabetes\u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and dietary fat, especially trans fatty acids and saturated fats, is positively associated with T2D and obesity risks due to its effects on insulin sensitivity\u003csup\u003e\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Consumption of soft drinks and foods that contain high amounts of added sugar were also found to increase risk for T2D and obesity, and limiting added sugar intake and UPFs may prevent chronic disease in children and adolescents\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Previous work and the findings from our present study suggest that the nutrients common to UPFs increase the risk for obesity, which may be a mechanism by which UPFs increase the risk for T2D.\u003c/p\u003e \u003cp\u003eThis study has many strengths. Firstly, gold-standard outcome measurements were obtained using OGTT and DEXA at both time points\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Secondly, our study focuses on young adults: an age group not often included in previous work. Young adults in their late teens and twenties have only recently reached physically mature and are undergoing significant lifestyle changes that may affect their risk for obesity and T2D. Thirdly, this study is one of the few longitudinal studies to examine the relationship between UPFs and risk for prediabetes, insulin resistance, and obesity, where diet and each outcome was assessed at each time point. This study design allowed us to evaluate the changes in UPF consumption over several years to follow up and assess the resulting impacts on glucose homeostasis and insulin sensitivity. However, this study also has some limitations. Our relatively small sample size may have limited our statistical power to detect associations between UPF consumption and some outcomes. Despite this, we did have enough power to consistently detect associations between UPF% consumption and prediabetes, IGT, and markers of insulin resistance. Additionally, dietary recalls may be subject to recall bias and may not represent long-term eating habits, though administration of multiple recalls improves estimates of nutrient intake and the variety of foods consumed\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We also used two different dietary recall systems, NDSR and ASA24, for the baseline and follow-up visits, which use different databases for reported foods. However, both systems provided a similar level of detail about food processing and sources, and we do not expect that one or the other would systematically encourage classification into higher NOVA processing categories\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. While it is possible that some misclassification of UPFs occurred, though we minimized this by preforming the classification using two independent researchers, and we would expect any misclassification to be non-differential.\u003c/p\u003e \u003cp\u003eFindings from this study suggest that reducing UPF consumption may reduce the risk for prediabetes and T2D in youth. Young adults may also benefit from limiting foods that contain high amounts of salt, fat, and sugar as they all potentially contribute to obesity, which also increases the risk for T2D\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Metabolic diseases such as T2D and obesity are significant public health problems and are becoming more prevalent among young adults\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,64\u003c/sup\u003e. Our study also shows that UPF consumption is associated with insulin resistance, a risk factor for T2D and a condition not commonly assessed in previous studies of UPFs and T2D risk. Because more than half of the total daily energy in consumption in the US is from UPFs, this modifiable risk factor is a possible target for both individual and public health interventions in preventing metabolic diseases. Future studies may incorporate additional methods of diet assessment and larger sample sizes to improve our understanding of the eating habits of young adults and the mechanisms underlying associations between UPF consumption and metabolic diseases.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis prospective study found that UPF consumption is positively associated with increased risk for prediabetes among young adults. Increasing UPF consumption was also associated with impaired glucose tolerance and insulin resistance, known risk factors for the future development of T2D. This study evaluated a unique population of the youth with detailed longitudinal measurements of diet and glucose homeostasis. These findings indicate that limiting the consumption of UPFs may be an important strategy for T2D prevention among young adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eY.L., E.C. and L.C designed research; E.C., W.B.P. and S.R. conducted research; Y.L. and E.C. performed statistical analysis; Y.L. and E.L. wrote the paper; Y.L. had primary responsibility for final content; W.B.P., Z.C., F.G., M.I.G., T.L.A., J.A.G., D.V.C, N.S. and L.C. reviewed and edited the paper. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eData described in the manuscript, code book, and analytic code will be made available upon request. The data are not publicly available to protect participants’ identifiable information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe results reported here in correspond to specific aims of grant R01ES029944 from the National Institute of Environmental Health Science (NIEHS). Funding for the MetaAir study came from the Southern California Children’s Environmental Health Center grants funded by NIEHS (5P01ES022845-03, P30ES007048, 5P01ES011627), the United States Environmental Protection Agency (RD83544101), and the Hastings Foundation. Additional funding from NIEHS supported Dr. Chatzi (R01ES029944, R01ES030364, U01HG013288, and P30ES007048), and Dr. Costello (T32ES013678 and U01HG013288). Other support from the European Union supported Dr. Chatzi (The Advancing Tools for Human Early Lifecourse Exposome Research and Translation (ATHLETE) project: 874583), Dr. Alderate (NIEHS: R01ES035035 and R01ES035056 and National Institute on Minority Health and Health Disparities: P50MD017344), and Dr. Stratakis (Horizon Europe Research and Innovation Program under the Marie Skłodowska-Curie Actions Postdoctoral Fellowships: 101059245).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process:\u003c/strong\u003e The authors did not use any AI tool during the preparation of this work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDivers J, Mayer-Davis EJ, Lawrence JM, Isom S, Dabelea D, Dolan L, et al. Trends in Incidence of Type 1 and Type 2 Diabetes Among Youths \u0026mdash; Selected Counties and Indian Reservations, United States, 2002\u0026ndash;2015. 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Am J Epidemiol. 2006;163(11):1053\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwj127\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwj127\" 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":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ultra-processed food, type 2 diabetes, prediabetes, young adults, body composition","lastPublishedDoi":"10.21203/rs.3.rs-6875960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6875960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eUltra-processed foods (UPFs), often high in sodium, sugar, and unhealthy fats, compose more than half of total dietary energy consumption in the United States. A diet composed of a high amount of UPFs can contribute to glucose dysregulation and insulin resistance, which may lead to prediabetes and type 2 diabetes (T2D). The goal of this study is to examine associations between UPF consumption and prediabetes and related biomarkers in youth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eYoung adults (n = 85) aged 17–22 years old from the Meta-AIR study, a subset of the Children’s Health Study, were enrolled between 2014–2018 and returned for a second visit between 2020–2022. Participants completed two 24-hour dietary recalls and an oral glucose tolerance test at each visit. Food items were categorized as either an UPF or non-UPF according to NOVA classification guidelines. The proportion of the diet composed of UPFs was calculated for each participant. Regression models were used to assess relationships of UPF consumption at baseline and change between visits with markers of glucose homeostasis at follow-up, adjusting for demographics and physical activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA 10 percentage-point increase in UPF consumption between visits was associated with a 64% (OR: 1.64, 95% Cl: 1.15, 2.50) higher risk for prediabetes and 56% (OR: 1.56, 95% CI: 1.42, 5.86) higher risk for impaired glucose tolerance at follow-up. Higher baseline UPF consumption was significantly positively associated with fasting insulin (\u003cem\u003eβ\u003c/em\u003e = 2.09, 95% CI: 0.06, 4.12), 2-hour insulin (\u003cem\u003eβ\u003c/em\u003e = 44.75, 95% CI: 22.26, 67.25) and insulin area under the curve (\u003cem\u003eβ\u003c/em\u003e = 63.19, 95% CI: 34.84, 91.54) at follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eUPF consumption may increase the risk for T2D among young adults. Our findings suggest that limiting UPF consumption could be an important strategy for T2D prevention in this population.\u003c/p\u003e","manuscriptTitle":"Ultra-Processed Food Intake is Associated with Altered Glucose Homeostasis in Young Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 09:10:35","doi":"10.21203/rs.3.rs-6875960/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-23T11:08:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T19:45:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T18:53:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T02:16:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5964177363117877038497360710395402393","date":"2025-06-30T10:12:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320795420632069418316639230807722019526","date":"2025-06-28T02:30:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277644190128708358257900875920873012001","date":"2025-06-27T23:52:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120807513478185254092426375918395175202","date":"2025-06-27T15:20:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159770888583494504416063260311224607655","date":"2025-06-27T14:58:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298911337343433644713022394270334508207","date":"2025-06-25T14:47:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263809107637019456841836713281656410738","date":"2025-06-25T14:40:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106662741841710236700486398367315207910","date":"2025-06-25T14:37:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-25T14:28:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T05:43:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-13T05:43:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition \u0026 Metabolism","date":"2025-06-12T03:02:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abb959c5-91cc-49f9-88c7-e8e391771f35","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:01:26+00:00","versionOfRecord":{"articleIdentity":"rs-6875960","link":"https://doi.org/10.1186/s12986-025-01036-6","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2025-11-10 15:57:46","publishedOnDateReadable":"November 10th, 2025"},"versionCreatedAt":"2025-07-01 09:10:35","video":"","vorDoi":"10.1186/s12986-025-01036-6","vorDoiUrl":"https://doi.org/10.1186/s12986-025-01036-6","workflowStages":[]},"version":"v1","identity":"rs-6875960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6875960","identity":"rs-6875960","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}