Precision Lifestyle Medicine Program for IFHAS Participants

NCT07742917 | Recruiting DMC

• 1 other identifier: DOH/ADHRTC/2026/1867
• Study Type: interventional
• Target: 150
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this clinical trial is to evaluate whether a precision lifestyle medicine intervention can improve cardiometabolic health in Emirati adults aged 25 to 50 years living in Abu Dhabi with obesity (BMI 30-35 kg/m²) who have previously completed IFHAS screening. The study will also use IFHAS screening data to better understand participants' health profiles and the relationship between lifestyle factors and chronic diseases. The main questions it aims to answer are:

• Does a precision lifestyle medicine program improve cardiometabolic biomarkers after an 18-week intervention?
• Can IFHAS screening data be used to identify health risk profiles and support personalized lifestyle interventions for the Emirati population? Researchers will compare participants receiving the precision lifestyle medicine intervention with a propensity-matched control group receiving standard IFHAS care to determine whether the intervention leads to greater improvements in cardiometabolic health. Participants will:
• Take part in an 18-week personalized lifestyle medicine program tailored to their health profile.
• Attend study visits and complete health assessments over a total study period of 24 weeks.
• Undergo cardiometabolic biomarker assessments before and after the intervention.
• Complete questionnaires or assessments about their adherence to the lifestyle program and their experience with the precision lifestyle medicine clinic.
• Provide biological samples to support the establishment of a biomarker repository for future research.

Conditions

Obese Adults; BMI/Body Composition; Cardiometabolic Risk; Cardiovascular Disease Risk; Type 2 Diabetes Risk

Keywords

cardiovascular; obesity; BMI; UAE; Emirati; IFHAS; Personalized; Precision Lifestyle Medicine; Emirati Population; Cardiovascular Disease Prevention; Type 2 Diabetes Prevention; Biological Age; Continuous Glucose Monitoring (CGM); Oura ring; Gut Microbiome; Preventive Medicine; Personalized Nutrition; Risk Stratification

Outcome Measures

Primary Outcomes (2)

• Change in Body Mass Index (BMI)

  Mean change in body mass index (BMI) from baseline to Week 24 following the precision lifestyle medicine intervention.

  Baseline and Week 24

• Change in Cardiometabolic Composite Response

  Proportion of participants classified as cardiometabolic responders at Week 24, defined as achieving at least two of the following: ≥3% reduction in body weight, ≥5% reduction in ApoB (or LDL-C), ≥0.3% reduction in HbA1c (or ≥15% reduction in HOMA-IR), ≥3 mmHg reduction in systolic blood pressure, or ≥2 cm reduction in waist circumference.

  24 weeks

Secondary Outcomes (9)

• High responder rate

  24 weeks

• Change in body weight

  baseline to week 24

• Change in HbA1c

  Baseline to Week 24

• Change in ApoB

  Baseline to Week 24

• VO₂peak

  Baseline to Week 24

Study Arms (2)

Precision Lifestyle Medicine Intervention

EXPERIMENTAL

Participants receive an 18-week precision lifestyle medicine intervention program based on IFHAS screening and risk stratification, including personalized lifestyle recommendations and clinical follow-up. Participants remain in the study for 24 weeks, with cardiometabolic outcomes assessed before and after the intervention.

Behavioral: Precision Lifestyle Medicine Program

Standard IFHAS Care (Matched Control)

ACTIVE COMPARATOR

Retrospective propensity score-matched control participants identified from the IFHAS cohort who receive standard IFHAS care and do not participate in the precision lifestyle medicine intervention. This comparison group is created using propensity score weighting to estimate intervention effects.

Other: Standard IFHAS Care

Interventions

Precision Lifestyle Medicine Program BEHAVIORAL

Participants receive an 18-week precision lifestyle medicine intervention based on individual IFHAS screening results and cardiometabolic risk stratification. The program includes personalized lifestyle recommendations and multidisciplinary clinical support focused on improving cardiometabolic health. Participants are followed for 24 weeks, with assessments conducted at baseline and post-intervention.

Precision Lifestyle Medicine Intervention

Standard IFHAS Care OTHER

Participants receive standard IFHAS care without participation in the precision lifestyle medicine intervention. This group serves as a propensity score-matched comparison cohort.

Standard IFHAS Care (Matched Control)

Eligibility Criteria

• Age: 25 Years - 50 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• Emiratis residing in Abu Dhabi emirate
• Aged 25-50 years
• BMI 30-35 kg/m²
• Thiqa cardholder
• Previous participation in IFHAS screening
• Able to provide informed consent
• Participant in the Emirati Genome Programme
• No weight-loss medications (e.g., Ozempic, Saxenda, Wegovy) within 3 months of enrollment.

You may not qualify if:

• Aged under 25 years or over 50 years of age
• BMI range under 30 kg/m2 and over 35 kg/m2
• Taking weight loss medication such as GLP-1 drugs (Ozempic, Saxenda, Wegovy)
• Active treatment for serious medical conditions (cancer, planned surgery, immunomodulation)
• Transplant surgery within 12 months
• Pregnancy
• Surgery within 3 months prior to recruitment
• Physical limitations preventing exercise participation
• Current enrolment in other lifestyle intervention programs
• Active psychiatric conditions, cognitive impairment, or mental health conditions affecting study participation
• Contraindication to MRI or DEXA imaging, including severe claustrophobia or need for sedation.

Sponsors & Collaborators

• Institute for Healthier Living Abu Dhabi: lead
• Abu Dhabi Public Health Centre: collaborator

Study Sites (1)

Institute for Healthier Living

Abu Dhabi, United Arab Emirates

RECRUITING

Related Publications (57)

• Fitzgerald KN, Hodges R, Hanes D, Stack E, Cheishvili D, Szyf M, Henkel J, Twedt MW, Giannopoulou D, Herdell J, Logan S, Bradley R. Potential reversal of epigenetic age using a diet and lifestyle intervention: a pilot randomized clinical trial. Aging (Albany NY). 2021 Apr 12;13(7):9419-9432. doi: 10.18632/aging.202913. Epub 2021 Apr 12.

  PMID: 33844651 BACKGROUND

• Reicher L, Bar N, Godneva A, Reisner Y, Zahavi L, Shahaf N, Dhir R, Weinberger A, Segal E. Phenome-wide associations of human aging uncover sex-specific dynamics. Nat Aging. 2024 Nov;4(11):1643-1655. doi: 10.1038/s43587-024-00734-9. Epub 2024 Nov 5.

  PMID: 39501126 BACKGROUND

• Lee S, Lee S, Kim Y, et al. A deep learning model for biological age prediction using comprehensive health check-up data: A retrospective cohort study. Aging (Albany NY). 2023;15(15):7519-7539.

  BACKGROUND

• Pinu FR, Beale DJ, Zwart P, et al. A random forest-based approach for the identification of diagnostic biomarkers for heart failure from gene expression data. Front Mol Biosci. 2020;7:139.

  BACKGROUND

• Alanazi SA, Kamruzzaman MM, Alruwaili M, et al. A machine learning-based approach for the prediction of cardiometabolic disease risk in students. Int J Environ Res Public Health. 2021;18(11):5649.

  BACKGROUND

• Ambrose M, Mitmesser SH, Sesso HD, et al. Use of reduced rank regression and random forest to identify dietary patterns associated with cardiometabolic risk. Curr Dev Nutr. 2017;1(6):e000782.

  BACKGROUND

• Georga EI, Protopappas VC, Yaluri N, et al. A machine learning approach for predicting non-responders to a lifestyle intervention for prediabetes. Sensors (Basel). 2021;21(6):2082.

  BACKGROUND

• Gárate-Escamila AK, El-Guindy A, Tamez-Peña JG. Machine learning for diabetes and hypertension classification in the Mexican population. J Healthc Eng. 2020;2020:8839074.

  BACKGROUND

• O'Gorman A, Gibbons H, O'Brien EC, et al. Use of metabolomics for assessing compliance to a raspberry intervention. Eur J Nutr. 2019;58(1):333-344.

  BACKGROUND

• Ulaszewska MM, Garcia-Aloy M, Llorach R, et al. A review of the application of metabolomics in human nutrition. Trends Food Sci Technol. 2019;84:80-92.

  BACKGROUND

• Meng, C., Zhang, J., He, M. et al. An overview of machine learning in precision nutrition. Signal Transduct Target Ther. 2023;8(1):337.

  BACKGROUND

• EuroQol. (1990). EQ-5D-3L. [online] Available at: https://euroqol.org/information-and-support/euroqol-instruments/eq-5d-3l/.

  BACKGROUND

• Bekairy AM, Bustami RT, Almotairi M, Jarab A, Katheri AM, Aldebasi TM, Aburuz S. Validity and reliability of the Arabic version of the the EuroQOL (EQ-5D). A study from Saudi Arabia. Int J Health Sci (Qassim). 2018 Mar-Apr;12(2):16-20.

  PMID: 29599689 BACKGROUND

• Antoun I, Alkhayer A, Aljabal M, Alkhayer A, Simon P, Mahfoud Y, Kotb A, Barker J, Mavilakandy A, Somani R, Ng GA, Zakkar M. The validity and reliability of the Arabic version of the EQ-5D in atrial fibrillation patients in a conflict country: a study from Syria. BMC Cardiovasc Disord. 2024 Oct 8;24(1):541. doi: 10.1186/s12872-024-04203-4.

  PMID: 39379843 BACKGROUND

• American College of Sports Medicine (2025). Physical Activity Guidelines. [online] ACSM. Available at: https://acsm.org/education-resources/trending-topics-resources/physical-activity-guidelines/.

  BACKGROUND

• Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): description of lifestyle intervention. Diabetes Care. 2002 Dec;25(12):2165-71. doi: 10.2337/diacare.25.12.2165.

  PMID: 12453955 BACKGROUND

• CDC (2024). About the Lifestyle Change Program. [online] National Diabetes Prevention Program.

  BACKGROUND

• American Diabetes Association Professional Practice Committee. 8. Obesity and Weight Management for the Prevention and Treatment of Type 2 Diabetes: Standards of Care in Diabetes-2025. Diabetes Care. 2025 Jan 1;48(1 Suppl 1):S167-S180. doi: 10.2337/dc25-S008.

  PMID: 39651976 BACKGROUND

• American Diabetes Association Professional Practice Committee. 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2025. Diabetes Care. 2025 Jan 1;48(1 Suppl 1):S86-S127. doi: 10.2337/dc25-S005.

  PMID: 39651983 BACKGROUND

• NICE (2025). Behavioural overweight and obesity management interventions | Overweight and obesity management | Guidance | NICE. [online] Nice.org.uk.

  BACKGROUND

• Argentieri MA, Xiao S, Bennett D, Winchester L, Nevado-Holgado AJ, Ghose U, Albukhari A, Yao P, Mazidi M, Lv J, Millwood I, Fry H, Rodosthenous RS, Partanen J, Zheng Z, Kurki M, Daly MJ, Palotie A, Adams CJ, Li L, Clarke R, Amin N, Chen Z, van Duijn CM. Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations. Nat Med. 2024 Sep;30(9):2450-2460. doi: 10.1038/s41591-024-03164-7. Epub 2024 Aug 8.

  PMID: 39117878 BACKGROUND

• Radtke MD, Chodur GM, Bissell MCS, Kemp LC, Medici V, Steinberg FM, Scherr RE. Validation of Diet ID in Predicting Nutrient Intake Compared to Dietary Recalls, Skin Carotenoid Scores, and Plasma Carotenoids in University Students. Nutrients. 2023 Jan 13;15(2):409. doi: 10.3390/nu15020409.

  PMID: 36678280 BACKGROUND

• Operation Manual Itamar Medical REF OM2196370. (n.d.). Available at: https://www.itamar-medical.com/wp-content/uploads/2019/11/WatchPAT-ONE-Operational-Manual-USA.pdf.

  BACKGROUND

• Massachusetts General Hospital. (2024). McCance Center for Brain Health Brain Care Score. [online]

  BACKGROUND

• Toh, C.M., et al. (2020) 'National Steps Challenge and Healthcare Savings in Singapore', Singapore Medical Journal, 43(7), pp. 333-9. PMID: 12437039.

  BACKGROUND

• Puska, P., et al. (2020) 'North Karelia Project: A Landmark in NCD Prevention', Health Promotion Practice, 14(5), pp. 495-499. doi: 10.1177/1559827620910981.

  BACKGROUND

• Elmusharaf K, Grafton D, Jung JS, Roberts E, Al-Farsi Y, Al Nooh AA, Bin Belaila B, ElShamy A, Al-Zuabi H, Al Mutawa KA, Alraisi S, Al Lawat N, Gharbal A, Alomary S, Kulikov A, Pourghazian N, Slama S, Tarlton D, Banatvala N. The case for investing in the prevention and control of non-communicable diseases in the six countries of the Gulf Cooperation Council: an economic evaluation. BMJ Glob Health. 2022 Jun;7(6):e008670. doi: 10.1136/bmjgh-2022-008670.

  PMID: 35649631 BACKGROUND

• UNDP (2024) UAE's proactive investments in primary and preventive care. Available at: https://www.undp.org/sites/g/files/zskgke326/files/2024-02/uae_phc_costing_report.pdf (Accessed: 13 November 2024).

  BACKGROUND

• Koornneef E, Robben P, Blair I. Progress and outcomes of health systems reform in the United Arab Emirates: a systematic review. BMC Health Serv Res. 2017 Sep 20;17(1):672. doi: 10.1186/s12913-017-2597-1.

  PMID: 28931388 BACKGROUND

• Abu Dhabi Healthcare Data Privacy Standard Doh Standard on Patient Healthcare Data Privacy. (2020). Available at: https://www.doh.gov.ae/-/media/Feature/Aamen/DOH-Standard-on-Patient-Healthcare-Data-Privacy.ashx

  BACKGROUND

• Academy of Nutrition and Dietetics ed., (2024). NUTRITION CARE PROCESS. [online] Available at: https://www.andeal.org/ncp.

  BACKGROUND

• The GlycanAge Team. How Accurate Are Biological Age Tests? 2024 [Available from: https://glycanage.com/.

  BACKGROUND

• Abdullah, K., Jacob, S., Hussain, Hamid Y. and Salim, N. (2019). Prediabetes Risk Assessment among Nurses Recruited in a Dubai Local Government Hospital: A Cross-Sectional Study. Dubai Diabetes and Endocrinology Journal, 25(1-2), pp.39-44. doi: https://doi.org/10.1159/000500913

  BACKGROUND

• Gabriel R, Acosta T, Florez K, Anillo L, Navarro E, Boukichou N, Acosta-Reyes J, Barengo NC, Lindstrom J, Tuomilehto JO, Aschner P. Validation of the Finnish Type 2 Diabetes Risk Score (FINDRISC) with the OGTT in Health Care Practices in Europe. Diabetes Res Clin Pract. 2021 Aug;178:108976. doi: 10.1016/j.diabres.2021.108976. Epub 2021 Jul 22.

  PMID: 34302911 BACKGROUND

• Şiyve , N., Emecen , A.N. and Ünal, B. (2023). Validity of Finnish diabetes risk score in Turkish population and developing a predictive model for Type 2 diabetes mellitus. Popul. Med.

  BACKGROUND

• Janghorbani M, Adineh H, Amini M. Evaluation of the Finnish Diabetes Risk Score (FINDRISC) as a screening tool for the metabolic syndrome. Rev Diabet Stud. 2013 Winter;10(4):283-92. doi: 10.1900/RDS.2013.10.283. Epub 2014 Feb 10.

  PMID: 24841881 BACKGROUND

• Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003 Mar;26(3):725-31. doi: 10.2337/diacare.26.3.725.

  PMID: 12610029 BACKGROUND

• NICE (2023). Cardiovascular disease: risk assessment and reduction, including lipid modification NICE guideline. [online] Available at: https://www.nice.org.uk/guidance/ng238/resources/cardiovascular-disease-risk-assessment-and-reduction-including-lipid-modification-pdf-66143902851781.

  BACKGROUND

• Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017 May 23;357:j2099. doi: 10.1136/bmj.j2099.

  PMID: 28536104 BACKGROUND

• ClinRisk. (2018). QRISK3. [online] Available at: https://www.qrisk.org/index.php.

  BACKGROUND

• Abdi, S., Sadiya, A., Ali, S., Varghese, S. and Abusnana, S. (2015). Behavioural Lifestyle Intervention Study (BLIS) in patients with type 2 diabetes in the United Arab Emirates: A randomized controlled trial. BMC Nutrition, [online] 1(1). doi:https://doi.org/10.1186/s40795-015-0028-4.

  BACKGROUND

• Rotunda W, Rains C, Jacobs SR, Ng V, Lee R, Rutledge S, Jackson MC, Myers K. Weight Loss in Short-Term Interventions for Physical Activity and Nutrition Among Adults With Overweight or Obesity: A Systematic Review and Meta-Analysis. Prev Chronic Dis. 2024 Apr 4;21:E21. doi: 10.5888/pcd21.230347.

  PMID: 38573796 BACKGROUND

• https://cambridgecognition.com/digital-cognitive-assessments/

  BACKGROUND

• https://www.pavingwellness.org/paving-program

  BACKGROUND

• Huang PL. A comprehensive definition for metabolic syndrome. Dis Model Mech. 2009 May-Jun;2(5-6):231-7. doi: 10.1242/dmm.001180.

  PMID: 19407331 BACKGROUND

• Vanderbilt.edu. (2024). Form B | wallstonk | projects | School of Nursing | Vanderbilt University. [online] https://nursing.vanderbilt.edu/projects/wallstonk/form_b.php [Accessed 11 Sep. 2024].

  BACKGROUND

• Ngufor C, Caraballo PJ, O'Byrne TJ, Chen D, Shah ND, Pruinelli L, Steinbach M, Simon G. Development and Validation of a Risk Stratification Model Using Disease Severity Hierarchy for Mortality or Major Cardiovascular Event. JAMA Netw Open. 2020 Jul 1;3(7):e208270. doi: 10.1001/jamanetworkopen.2020.8270.

  PMID: 32678448 BACKGROUND

• Mezhal F, Oulhaj A, Abdulle A, AlJunaibi A, Alnaeemi A, Ahmad A, Leinberger-Jabari A, Al Dhaheri AS, Tuzcu EM, AlZaabi E, Al-Maskari F, Alanouti F, Alameri F, Alsafar H, Alblooshi H, Alkaabi J, Wareth LA, Aljaber M, Kazim M, Weitzman M, Al-Houqani M, Ali MH, Oumeziane N, El-Shahawy O, Al-Rifai RH, Scherman S, Shah SM, Loney T, Almahmeed W, Idaghdour Y, Ahmed LA, Ali R. The interrelationship and accumulation of cardiometabolic risk factors amongst young adults in the United Arab Emirates: The UAE Healthy Future Study. Diabetol Metab Syndr. 2021 Nov 27;13(1):140. doi: 10.1186/s13098-021-00758-w.

  PMID: 34838113 BACKGROUND

• Jonas DE, Reddy S, Middleton JC, Barclay C, Green J, Baker C, Asher G. Screening for Cardiovascular Disease Risk With Electrocardiography: An Evidence Review for the U.S. Preventive Services Task Force [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2018 Jun. Report No.: 17-05235-EF-1. Available from http://www.ncbi.nlm.nih.gov/books/NBK525432/

  PMID: 30212062 BACKGROUND

• SM Ayaz Zakir (2024). UAE: Cancer among younger residents on the rise, say doctors - News | Khaleej Times. [online] Khaleej Times. Available at: https://www.khaleejtimes.com/lifestyle/health/uae-cancer-among-younger-residents-on-the-rise-say-doctors

  BACKGROUND

• World Health Organization. (2018). Noncommunicable diseases country profiles 2018. World Health Organization. https://apps.who.int/iris/handle/10665/274512.

  BACKGROUND

• Bardsley, D. (2024). UAE's healthcare bill for diabetes could soar to $3.4 billion a year, study suggests. [online] The National. Available at: https://www.thenationalnews.com/uae/2024/03/03/uaes-healthcare-bill-for-diabetes-could-soar-to-34-billion-a-year-study-suggests/.

  BACKGROUND

• CDC (2024). Health and Economic Benefits of Breast Cancer Interventions. [online] National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP). Available at: https://www.cdc.gov/nccdphp/priorities/breast-cancer.html.

  BACKGROUND

• Ferlay J, Ervik M, Lam F, Laversanne M, Colombet M, Mery L, Piñeros M, Znaor A, Soerjomataram I, Bray F (2024). Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. Available from: https://gco.iarc.who.int/today

  BACKGROUND

• Herman WH, Ye W, Griffin SJ, Simmons RK, Davies MJ, Khunti K, Rutten GE, Sandbaek A, Lauritzen T, Borch-Johnsen K, Brown MB, Wareham NJ. Early Detection and Treatment of Type 2 Diabetes Reduce Cardiovascular Morbidity and Mortality: A Simulation of the Results of the Anglo-Danish-Dutch Study of Intensive Treatment in People With Screen-Detected Diabetes in Primary Care (ADDITION-Europe). Diabetes Care. 2015 Aug;38(8):1449-55. doi: 10.2337/dc14-2459. Epub 2015 May 18.

  PMID: 25986661 BACKGROUND

• Fragala MS, Shiffman D, Birse CE. Population health screenings for the prevention of chronic disease progression. Am J Manag Care. 2019 Nov;25(11):548-553.

  PMID: 31747233 BACKGROUND

• IFHAS Abu Dhabi Public Health Centre. Why Is Screening Important?

  BACKGROUND

MeSH Terms

Conditions

Obesity

Condition Hierarchy (Ancestors)

Overweight; Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Nicole Sirotin, MD

  Institute for Healthier Living Abu Dhabi

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Erik Koornneef, PhD

CONTACT

+971500888594; e.koornneef@ihlad.ae

Leanne Cassidy, MSc

CONTACT

+971559168075; l.cassidy@ihlad.ae

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Quasi-experimental intervention with a propensity score-weighted matched control group (150 intervention participants and 150 matched controls).

Study Record Dates

• First Submitted: July 29, 2026
• First Posted: August 3, 2026
• Study Start: August 3, 2026
• Primary Completion (Estimated): June 13, 2027
• Study Completion (Estimated): December 13, 2027
• Last Updated: August 3, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

AE (1)