ClinicalTrials.gg
RecruitingNCT07175701ULTRA-CGMUpdated Apr 17, 2026

Ultra-processed Food-reducing Intervention and Continuous Glucose Monitoring

An interventional study of 40-minute one-on-one dietary counseling and nutrition education aimed at reducing ultra-processed food consumption and Standard dietary counseling and nutrition education based on national guidelines in Postprandial Glucose, Glucose Levels and Hyperglycemias, sponsored by Korea University. Recruiting at 1 site in South Korea. Open to participants aged 20 Years to 39 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-04-17.

Sponsored by Korea University · Not applicable, Interventional, and Prevention

From the registry’s dates

  • Started Oct 2025; still recruiting 1 year 1 month later.
Phase
Not applicable
Study type
Interventional
Enrollment
50
Allocation
Randomized
Ages
20 Years to 39 Years
Sex
All
01

Study summary

The goal of this trial is to investigate whether reduction in ultra-processed food intake through dietary counseling and education can improve postprandial glucose responses and glycemic variability compared with a usual diet in young, healthy Korean adults aged 20-30 years. The main questions it aims to answer are:

- Does reducing ultra-processed food intake, while maintaining total energy intake and usual lifestyle behaviors, improve postprandial glucose and lower glycemic variability in healthy adults without diabetes?

Participants will:

  • Undergo the 10-day pre-intervention monitoring period, during which each participant will wear a continuous glucose monitoring (CGM) device and concurrently report their daily dietary intakes (all food and beverage consumption) and other lifestyle behaviors (sleep, smoking, physical activity)
  • After the 10-day pre-intervention monitoring period, participants will be randomized to either intervention or control group
  • Intervention group: Participants will visit the research site to receive dietitian-led one-on-one nutrition education and personalized dietary counseling targeting reduction of ultra-processed food intake. Personalized diet counseling will be provided by study dietitian based on participant's records of dietary intakes during the 10-day pre-intervention monitoring period.
  • Control group: Participants will receive dietitian-led nutrition education and personalized dietary counseling based on the national dietary guidelines.
  • After the intervention, participants will undergo the 10-day post-intervention monitoring period, during which participants will wear a new CGM device for an additional 10 days and continue daily reporting of dietary intakes (all food and beverage consumptions) and lifestyle behaviors (sleep, smoking, physical activity).
Read the detailed description

The Ultra-Processed Food Reduction Intervention and Continuous Glucose Monitoring (ULTRA-CGM) trial is a parallel, two-arm randomized controlled trial designed to evaluate the effects of a ultra-processed food (UPF)-reducing dietary intervention on postprandial glucose responses and glycemic variability in young Korean adults (aged 20-39 years) using CGM devices. Participants will be randomly assigned to either a UPF-reducing intervention group or a control group maintaining their usual diet. We hypothesize that receiving the UPF-reducing dietary intervention will improve postprandial glucose responses and reduce glycemic variability compared with participants receiving standard dietary counseling based on national guidelines. The findings from this trial may provide experimental evidence on the metabolic effects of reducing UPF consumption and inform dietary strategies for diabetes prevention among younger adults.

02

Conditions studied

  • Postprandial Glucose
  • Glucose Levels
  • Hyperglycemias

Keywords

  • ultra-processed food
  • continuous glucose monitoring
  • processed food
  • glucose
  • glycemic variability
  • glycemic control
  • hyperglycemia
  • glucose intolerance
  • Food Additives
  • flavoring agents
  • sweetening agents
  • non-nutritive sweeteners
  • nutritive sweeteners
  • high fructose corn syrup
  • food coloring agents
  • food preservatives
03

In context

Hyperglycemia

800 studies on the registry are indexed under Hyperglycemia; 115 are open to participants now.

This study's planned enrollment of 50 is close to the median of 50 across 620 interventional studies indexed under Hyperglycemia.

Browse Hyperglycemia studies →

Lead sponsor

Korea University is the lead sponsor of 64 studies on the registry; 7 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
20 Years to 39 Years
Sexes eligible
All
Accepts healthy volunteers
Yes

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  1. Korean adults aged 20-39 years
  2. Not currently pregnant
  3. No personal history of diabetes or other glucose-related conditions
  4. Consume ≥25% of daily energy intake from UPFs, as assessed by an online, self-administered semi-quantitative screening food frequency questionnaire (FFQ)
  5. No restriction on wearing a CGM device
  6. No increased risk of bleeding
  7. No prior adverse reaction to CGM devices
  8. Willingness to participate in follow-up assessments

Exclusion criteria

Exclusion Criteria:

  1. Unable to maintain continuous follow-up during the study period (e.g., due to special plans such as traveling)
  2. Planning to follow a special or restrictive diet (e.g., for weight loss) during the study period
05

Study design

Phase
Not applicable
Primary purpose
Prevention
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Double (Participant, Outcomes assessor)
Enrollment
50 participants (estimated)

Study arms

  • Experimental
    Intervention group

    Participants will be randomly assigned to the intervention group using block randomization with a 1:1 allocation ratio.

    Behavioral: 40-minute one-on-one dietary counseling and nutrition education aimed at reducing ultra-processed food consumption

  • Active comparator
    Control group

    Participants will be randomly assigned to the control group using block randomization with a 1:1 allocation ratio.

    Behavioral: Standard dietary counseling and nutrition education based on national guidelines

Interventions

  • Behavioral40-minute one-on-one dietary counseling and nutrition education aimed at reducing ultra-processed food consumption

    Participants assigned to the intervention group receive a 40-minute one-on-one nutrition education session and personalized dietary counseling delivered by the study dietitian. The goals of these sessions are to reduce UPF consumption while maintaining total energy intake and usual lifestyle behaviors. All sessions follow a standardized protocol to ensure consistent delivery of the intervention. The study dietitian delivering the intervention receives training on the standardized protocol prior to participant enrollment.

  • BehavioralStandard dietary counseling and nutrition education based on national guidelines

    Participants assigned to the control group are also provided with a 40-minute one-on-one nutrition education session and personalized dietary counseling, but with different objectives. During these sessions, the national dietary guidelines are introduced using an educational leaflet distributed by the Ministry of Health and Welfare. The education session and counseling provided to the control group do not include any information on UPFs. After completion of the study, participants in the control group are provided with the same nutrition education materials on UPF reduction that are used in the intervention group.

06

What researchers measure

Primary outcomes

  1. Change in postprandial glucose responses

    The primary outcome of this study is the change in postprandial glucose responses from the baseline monitoring period to the post-intervention monitoring period. Postprandial glucose responses are assessed using CGM and quantified as the incremental area under the curve (iAUC) over 2 hours following each meal. Higher iAUC values indicate greater postprandial glucose excursions. For each meal, iAUC is calculated as the area under the curve above the baseline level over the 2-hour postprandial period, using the trapezoidal rule. The baseline glucose level is defined as the mean glucose value in the 5 minutes immediately preceding meal consumption. For overlapping meals, the iAUC for the first meal is calculated from its start until 2 hours after, and overlapping periods are excluded from subsequent meals' iAUC to ensure each glucose excursion is counted once.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

Secondary outcomes

  1. Change in coefficient of variation (CV)

    CV indicates relative glycemic variability and is calculated as the standard deviation of glucose measurements assessed using CGM divided by the mean glucose level.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  2. Change in standard deviation (mg/dL)

    Standard deviation represents the absolute magnitude of glycemic variability, reflecting the dispersion of glucose values over time, assessed using CGM.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  3. Change in mean amplitude of glycemic excursions (MAGE)

    Mean amplitude of glycemic excursions represents the mean amplitude of major glucose excursions. It is calculate by mean height of glycemic excursions (peaks and nadirs) that exceed one SD of the mean glucose over a 24-hour period.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  4. Change in glucose spike frequency

    Glucose spike frequency quantifies the number of distinct glucose excursions above 140 mg/dL, providing an additional measure of postprandial hyperglycemia.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  5. Change in time-in-range (%)

    Time-in-range represents the percentage of time spent within the consensus target glucose range of 70-180 mg/dL, as defined by international continuous glucose monitoring consensus recommendations.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  6. Change in time-in-tight-range (%)

    Time-in-tight-range represents the percentage of time spent within the tighter target range of 70-140 mg/dL. Because participants are healthy young adults, time-in-tight-range 70-140 mg/dL is considered.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  7. Change in time-above-range (%)

    Time-above-range represents the percentage of time spent with glucose levels above 180 mg/dL or 140 mg/dL. Because participants are healthy young adults, time-above-range 140 mg/dL is considered.

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  8. Change in time-below-range (%)

    Time-below-range represents the percentage of time spent with glucose levels below 70 mg/dL

    Time frame: Change in mean values from the baseline monitoring period (10-day period before the intervention) to the post-intervention monitoring period (10-day period immediately following the intervention)

  9. Change in body weight (kg)

    Body weight will be directly measured by study staff to assess overall adiposity.

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

  10. Change in waist circumference (cm)

    Waist circumference will be directly measured by study staff to assess central adiposity.

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

  11. Change in fat mass (kg)

    Fat mass will be measured using a bioelectrical impedance analyzer (BIA) (InBody 270S; InBody Co., Ltd, Seoul, Korea).

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

  12. Change in muscle mass (kg)

    Muscle mass will be measured using a bioelectrical impedance analyzer (BIA) (InBody 270S; InBody Co., Ltd, Seoul, Korea).

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

  13. Change in percentage of body fat (%)

    Percentage of body fat will be measured using a bioelectrical impedance analyzer (BIA) (InBody 270S; InBody Co., Ltd, Seoul, Korea).

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

  14. Change in blood pressure measurements (mmHg)

    Blood pressures measurements, including both systolic and diastolic values, are measured to capture potential cardiometabolic effects of the intervention. Blood pressure is measured by a trained study nurse (a registered nurse) on the upper arm using an automated blood pressure monitoring device (WatchBP office Target BP3MD1-4; Microlife AG, Switzerland) while participants are seated and at rest. The mean of three consecutive measurements is used for analysis.

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

  15. Change in handgrip strength (kg)

    Handgrip strength is assessed as a marker of muscular strength and functional status. Handgrip strength is assessed using a handgrip dynamometer (T.K.K.-5401; Takei Scientific Instruments Co., Ltd, Tokyo, Japan). Handgrip measurements are performed twice for each hand in an alternating manner, and the highest value is used for analysis

    Time frame: Change from study enrollment (day 1) to study completion (day 23; 10 days after the intervention)

07

Study locations

1 of 1 sites recruiting
  • Health Promotion Lab, College of Health Science, Korea University
    Seoul, Seoul 02841, South Korea
    Recruiting
    • Yujin Kim, BSN · Contactiamyujin@korea.ac.kr · +82-10-8712-9763
    • Hannah Oh, ScD · Principal investigator
08

References and documents

Publications

  • Moher D, Schulz KF, Altman DG; CONSORT Group. The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomised trials. Clin Oral Investig. 2003 Mar;7(1):2-7. doi: 10.1007/s00784-002-0188-x. Epub 2003 Jan 31. PubMed 12673431 ↗
  • Imai S, Fukui M, Ozasa N, Ozeki T, Kurokawa M, Komatsu T, Kajiyama S. Eating vegetables before carbohydrates improves postprandial glucose excursions. Diabet Med. 2013 Mar;30(3):370-2. doi: 10.1111/dme.12073. No abstract available. PubMed 23167256 ↗
  • O'Keefe JH, Bell DS. Postprandial hyperglycemia/hyperlipidemia (postprandial dysmetabolism) is a cardiovascular risk factor. Am J Cardiol. 2007 Sep 1;100(5):899-904. doi: 10.1016/j.amjcard.2007.03.107. Epub 2007 Jun 26. PubMed 17719342 ↗
  • Borg R, Kuenen JC, Carstensen B, Zheng H, Nathan DM, Heine RJ, Nerup J, Borch-Johnsen K, Witte DR; ADAG Study Group. Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study. Diabetologia. 2010 Aug;53(8):1608-11. doi: 10.1007/s00125-010-1741-9. Epub 2010 Apr 16. PubMed 20396998 ↗
  • Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group; Fox LA, Beck RW, Xing D. Variation of interstitial glucose measurements assessed by continuous glucose monitors in healthy, nondiabetic individuals. Diabetes Care. 2010 Jun;33(6):1297-9. doi: 10.2337/dc09-1971. Epub 2010 Mar 9. PubMed 20215454 ↗
  • Shah VN, DuBose SN, Li Z, Beck RW, Peters AL, Weinstock RS, Kruger D, Tansey M, Sparling D, Woerner S, Vendrame F, Bergenstal R, Tamborlane WV, Watson SE, Sherr J. Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study. J Clin Endocrinol Metab. 2019 Oct 1;104(10):4356-4364. doi: 10.1210/jc.2018-02763. PubMed 31127824 ↗
  • Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, Bosi E, Buckingham BA, Cefalu WT, Close KL, Cobelli C, Dassau E, DeVries JH, Donaghue KC, Dovc K, Doyle FJ 3rd, Garg S, Grunberger G, Heller S, Heinemann L, Hirsch IB, Hovorka R, Jia W, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Levine B, Mayorov A, Mathieu C, Murphy HR, Nimri R, Norgaard K, Parkin CG, Renard E, Rodbard D, Saboo B, Schatz D, Stoner K, Urakami T, Weinzimer SA, Phillip M. Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range. Diabetes Care. 2019 Aug;42(8):1593-1603. doi: 10.2337/dci19-0028. Epub 2019 Jun 8. PubMed 31177185 ↗
  • Song J, Oh TJ, Song Y. Individual Postprandial Glycemic Responses to Meal Types by Different Carbohydrate Levels and Their Associations with Glycemic Variability Using Continuous Glucose Monitoring. Nutrients. 2023 Aug 13;15(16):3571. doi: 10.3390/nu15163571. PubMed 37630761 ↗
  • Mendoza K, Smith-Warner SA, Rossato SL, Khandpur N, Manson JE, Qi L, Rimm EB, Mukamal KJ, Willett WC, Wang M, Hu FB, Mattei J, Sun Q. Ultra-processed foods and cardiovascular disease: analysis of three large US prospective cohorts and a systematic review and meta-analysis of prospective cohort studies. Lancet Reg Health Am. 2024 Sep 2;37:100859. doi: 10.1016/j.lana.2024.100859. eCollection 2024 Sep. PubMed 39286398 ↗
  • Wang M, Du X, Huang W, Xu Y. Ultra-processed Foods Consumption Increases the Risk of Hypertension in Adults: A Systematic Review and Meta-analysis. Am J Hypertens. 2022 Oct 3;35(10):892-901. doi: 10.1093/ajh/hpac069. PubMed 35750049 ↗
  • Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, Kojima T, Kuzuya M, Lee JSW, Lee SY, Lee WJ, Lee Y, Liang CK, Lim JY, Lim WS, Peng LN, Sugimoto K, Tanaka T, Won CW, Yamada M, Zhang T, Akishita M, Arai H. Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc. 2020 Mar;21(3):300-307.e2. doi: 10.1016/j.jamda.2019.12.012. Epub 2020 Feb 4. PubMed 32033882 ↗
  • Kim DW, Song S, Lee JE, Oh K, Shim J, Kweon S, Paik HY, Joung H. Reproducibility and validity of an FFQ developed for the Korea National Health and Nutrition Examination Survey (KNHANES). Public Health Nutr. 2015 Jun;18(8):1369-77. doi: 10.1017/S1368980014001712. Epub 2014 Aug 28. PubMed 25167205 ↗
  • Jung S, Park S, Kim JY. Comparison of dietary share of ultra-processed foods assessed with a FFQ against a 24-h dietary recall in adults: results from KNHANES 2016. Public Health Nutr. 2022 May;25(5):1166-1175. doi: 10.1017/S1368980022000179. Epub 2022 Jan 19. PubMed 35042567 ↗
  • Chan AW, Boutron I, Hopewell S, Moher D, Schulz KF, Collins GS, Tunn R, Aggarwal R, Berkwits M, Berlin JA, Bhandari N, Butcher NJ, Campbell MK, Chidebe RCW, Elbourne DR, Farmer AJ, Fergusson DA, Golub RM, Goodman SN, Hoffmann TC, Ioannidis JPA, Kahan BC, Knowles RL, Lamb SE, Lewis S, Loder E, Offringa M, Ravaud P, Richards DP, Rockhold FW, Schriger DL, Siegfried NL, Staniszewska S, Taylor RS, Thabane L, Torgerson DJ, Vohra S, White IR, Hrobjartsson A. SPIRIT 2025 statement: Updated guideline for protocols of randomised trials. PLoS Med. 2025 Apr 28;22(4):e1004589. doi: 10.1371/journal.pmed.1004589. eCollection 2025 Apr. PubMed 40294521 ↗
  • Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, Beck R, Bosi E, Buckingham B, Cobelli C, Dassau E, Doyle FJ 3rd, Heller S, Hovorka R, Jia W, Jones T, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Maahs D, Murphy HR, Norgaard K, Parkin CG, Renard E, Saboo B, Scharf M, Tamborlane WV, Weinzimer SA, Phillip M. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care. 2017 Dec;40(12):1631-1640. doi: 10.2337/dc17-1600. PubMed 29162583 ↗
  • Chen F, Huang K, Long Q, Ma M, Zhang T, Dong G, Wu W, Ni Y, Hui CC, Fu J. Comparative dietary effectiveness of a modified government-recommended diet with avoidance of ultra-processed foods on weight and metabolic management in children and adolescents: An open-label, randomized study. Asia Pac J Clin Nutr. 2022;31(2):282-293. doi: 10.6133/apjcn.202206_31(2).0014. PubMed 35766564 ↗
  • Dicken SJ, Jassil FC, Brown A, Kalis M, Stanley C, Ranson C, Ruwona T, Qamar S, Buck C, Mallik R, Hamid N, Bird JM, Brown A, Norton B, Gandini Wheeler-Kingshott CAM, Hamer M, van Tulleken C, Hall KD, Fisher A, Makaronidis J, Batterham RL. Ultraprocessed or minimally processed diets following healthy dietary guidelines on weight and cardiometabolic health: a randomized, crossover trial. Nat Med. 2025 Oct;31(10):3297-3308. doi: 10.1038/s41591-025-03842-0. Epub 2025 Aug 4. PubMed 40760353 ↗
  • Zhang L, Ding Y, Zhang B, Hu X. Analysis of the correlation between glycemic variability indices and disease severity as well as inflammatory status in patients with type 2 diabetes mellitus and periodontitis. Clin Oral Investig. 2025 Oct 14;29(11):508. doi: 10.1007/s00784-025-06609-6. PubMed 41085750 ↗
  • Ohara M, Fukui T, Ouchi M, Watanabe K, Suzuki T, Yamamoto S, Yamamoto T, Hayashi T, Oba K, Hirano T. Relationship between daily and day-to-day glycemic variability and increased oxidative stress in type 2 diabetes. Diabetes Res Clin Pract. 2016 Dec;122:62-70. doi: 10.1016/j.diabres.2016.09.025. Epub 2016 Oct 14. PubMed 27810687 ↗
  • Saisho Y. Glycemic variability and oxidative stress: a link between diabetes and cardiovascular disease? Int J Mol Sci. 2014 Oct 13;15(10):18381-406. doi: 10.3390/ijms151018381. PubMed 25314300 ↗
  • Jarvis PRE, Cardin JL, Nisevich-Bede PM, McCarter JP. Continuous glucose monitoring in a healthy population: understanding the post-prandial glycemic response in individuals without diabetes mellitus. Metabolism. 2023 Sep;146:155640. doi: 10.1016/j.metabol.2023.155640. Epub 2023 Jun 24. PubMed 37356796 ↗
  • Simmons AL, Schlezinger JJ, Corkey BE. What Are We Putting in Our Food That Is Making Us Fat? Food Additives, Contaminants, and Other Putative Contributors to Obesity. Curr Obes Rep. 2014 Jun 1;3(2):273-85. doi: 10.1007/s13679-014-0094-y. PubMed 25045594 ↗
  • Cho Y, Ryu S, Kim R, Shin MJ, Oh H. Ultra-processed Food Intake and Risk of Type 2 Diabetes in Korean Adults. J Nutr. 2024 Jan;154(1):243-251. doi: 10.1016/j.tjnut.2023.11.021. Epub 2023 Nov 24. PubMed 38007182 ↗
  • Kim Y, Cho Y, Koo B, Chen Z, Sun Q, Oh H. Ultra-processed food intake and risk of type 2 diabetes: a pooled analysis of three prospective cohorts of Korean adults and an updated meta-analysis. Eur J Epidemiol. 2025 Nov;40(11):1293-1305. doi: 10.1007/s10654-025-01297-8. Epub 2025 Sep 16. PubMed 40956553 ↗
  • Kim Y, Cho Y, Kim JE, Lee DH, Oh H. Ultra-Processed Food Intake and Risk of Type 2 Diabetes Mellitus: A Dose-Response Meta-Analysis of Prospective Studies. Diabetes Metab J. 2025 Sep;49(5):1064-1074. doi: 10.4093/dmj.2024.0706. Epub 2025 Jun 9. PubMed 40490026 ↗
  • Moradi S, Entezari MH, Mohammadi H, Jayedi A, Lazaridi AV, Kermani MAH, Miraghajani M. Ultra-processed food consumption and adult obesity risk: a systematic review and dose-response meta-analysis. Crit Rev Food Sci Nutr. 2023;63(2):249-260. doi: 10.1080/10408398.2021.1946005. Epub 2021 Jun 30. PubMed 34190668 ↗
  • Liu J, Steele EM, Li Y, Karageorgou D, Micha R, Monteiro CA, Mozaffarian D. Consumption of Ultraprocessed Foods and Diet Quality Among U.S. Children and Adults. Am J Prev Med. 2022 Feb;62(2):252-264. doi: 10.1016/j.amepre.2021.08.014. Epub 2021 Nov 6. PubMed 34753645 ↗
  • Martini D, Godos J, Bonaccio M, Vitaglione P, Grosso G. Ultra-Processed Foods and Nutritional Dietary Profile: A Meta-Analysis of Nationally Representative Samples. Nutrients. 2021 Sep 27;13(10):3390. doi: 10.3390/nu13103390. PubMed 34684391 ↗
  • Lee H, Yim Y, Cho Y, Oh J, Kim S, Son Y, Jo H, Hong S, Rahmati M, Cho H, Hwang J, Yon DK. Long-term trends and patterns in ultra-processed food consumption among Korean adults from 1998 to 2022. Sci Rep. 2025 Feb 3;15(1):4131. doi: 10.1038/s41598-025-88489-0. PubMed 39901034 ↗
  • Jung S, Kim JY, Park S. Eating patterns in Korean adults, 1998-2018: increased energy contribution of ultra-processed foods in main meals and snacks. Eur J Nutr. 2024 Feb;63(1):279-289. doi: 10.1007/s00394-023-03258-x. Epub 2023 Nov 24. PubMed 37999737 ↗
  • Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, Khandpur N, Cediel G, Neri D, Martinez-Steele E, Baraldi LG, Jaime PC. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019 Apr;22(5):936-941. doi: 10.1017/S1368980018003762. Epub 2019 Feb 12. PubMed 30744710 ↗
  • Jo G, Park D, Lee J, Kim R, Subramanian SV, Oh H, Shin MJ. Trends in Diet Quality and Cardiometabolic Risk Factors Among Korean Adults, 2007-2018. JAMA Netw Open. 2022 Jun 1;5(6):e2218297. doi: 10.1001/jamanetworkopen.2022.18297. PubMed 35731513 ↗
  • Oh H, Jo G, Kim OY, Lim H, Song S, Choi JH, Bae JH, Jin ES, Kim R, Lee Y, Jeong IK, Shin MJ; Committee of Nutrition and Public Relation of the Korean Society of Lipid and Atherosclerosis. Fact sheet: nationwide trends in dietary intakes among Korean adults, 2013-2022. Korean J Intern Med. 2025 May;40(3):427-437. doi: 10.3904/kjim.2024.337. Epub 2025 Mar 25. PubMed 40129190 ↗
  • Yang YS, Han K, Sohn TS, Kim NH. Young-onset type 2 diabetes in South Korea: a review of the current status and unmet need. Korean J Intern Med. 2021 Sep;36(5):1049-1058. doi: 10.3904/kjim.2021.379. Epub 2021 Sep 1. PubMed 34503316 ↗
  • Lascar N, Brown J, Pattison H, Barnett AH, Bailey CJ, Bellary S. Type 2 diabetes in adolescents and young adults. Lancet Diabetes Endocrinol. 2018 Jan;6(1):69-80. doi: 10.1016/S2213-8587(17)30186-9. Epub 2017 Aug 25. PubMed 28847479 ↗
  • Kim JY, Lee J, Moon JH, Park SE, Ko SH, Choi SH, Kim NH. Prevalence, Incidence, and Metabolic Characteristics of Young Adults with Type 2 Diabetes Mellitus in South Korea (2010-2020). Diabetes Metab J. 2025 Mar;49(2):172-182. doi: 10.4093/dmj.2024.0826. Epub 2025 Mar 1. PubMed 40073905 ↗
  • Kim JY, Park S, Park M, Kim NH, Kim SG. Income-Related Disparities in Mortality Among Young Adults With Type 2 Diabetes. JAMA Netw Open. 2024 Nov 4;7(11):e2443918. doi: 10.1001/jamanetworkopen.2024.43918. PubMed 39531234 ↗
  • Misra S, Ke C, Srinivasan S, Goyal A, Nyriyenda MJ, Florez JC, Khunti K, Magliano DJ, Luk A. Current insights and emerging trends in early-onset type 2 diabetes. Lancet Diabetes Endocrinol. 2023 Oct;11(10):768-782. doi: 10.1016/S2213-8587(23)00225-5. Epub 2023 Sep 11. PubMed 37708901 ↗
  • GBD 2021 Diabetes Collaborators. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023 Jul 15;402(10397):203-234. doi: 10.1016/S0140-6736(23)01301-6. Epub 2023 Jun 22. PubMed 37356446 ↗

Individual participant data

Plan to share: No — To protect personal information, data files containing personally identifiable information will be accessible only to designated researchers, and any external transfer of related data will be strictly prohibited.

09

Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Started Oct 2025 (actual). Primary completion is estimated for Aug 2026, 10 months after the start.

Of 208 non-industry interventional studies with no phase under Hyperglycemia, half reached primary completion within 14 months of starting (the median), counting those still running; a quarter within 9 and three quarters within 27. This study's planned 10 months is below that.

Counting every sponsor, half of 225 studies reached it within 14 months; a quarter within 7 and three quarters within 27.

How these figures are worked out, and which studies are left out of them.

Registered
Sep 5, 2025
First posted
Sep 16, 2025
StartActual
Oct 11, 2025
Primary completionEst.
Aug 31, 2026
CompletionEst.
Aug 31, 2026
Last update
Apr 17, 2026
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Apr 17, 2026, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07175701
Lead sponsor
Korea University
Collaborators
Korean Society of Cardiometabolic Syndrome (funder)
Responsible party
Hannah Oh (Associate Professor, Korea University) — Principal investigator
First posted
Sep 16, 2025
Start date
Oct 11, 2025
Primary completion
Aug 31, 2026 (estimated)
Completion
Aug 31, 2026 (estimated)
Last update
Apr 17, 2026

Study contacts

Yujin Kim, BSN
Contact
iamyujin@korea.ac.kr
+82-10-8712-9763
Hannah Oh, ScD
principal investigator · Department of Health Policy and Management, Korea University, Seoul, Republic of Korea

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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