Protocol
- Niteesh K Choudhry1, MD, PhD ;
- Shweta Priyadarshini2, PhD ;
- Jaganath Swamy2, MBA ;
- Mridul Mehta2, PhD
1Department of Medicine, Harvard Medical School, Boston, MA, United States
2Decipher Health, Delhi, India
Corresponding Author:
Niteesh K Choudhry, MD, PhD
Department of Medicine
Harvard Medical School
1620 Tremont Street, Suite 3030
Boston, MA, 02115
United States
Phone: 1 617 278 0412
Email: nkc@post.harvard.edu
Abstract
Background: Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the world’s most populous country, India. Nutrition is a critical and evidence-based component of effective blood glucose control and most dietary advice emphasizes carbohydrate and calorie reduction. Emerging global evidence demonstrates marked interindividual differences in postprandial glucose response (PPGR) although no such data exists in India and previous studies have primarily evaluated PPGR variation in individuals without diabetes.
Objective: This prospective cohort study seeks to characterize the PPGR variability among individuals with diabetes living in India and to identify factors associated with these differences.
Methods: Adults with T2D and a hemoglobin A1c of ≥7 are being enrolled from 14 sites around India. Participants wear a continuous glucose monitor, eat a series of standardized meals, and record all free-living foods, activities, and medication use for a 14-day period. The study’s primary outcome is PPGR, calculated as the incremental area under the curve 2 hours after each logged meal.
Results: Data collection commenced in May 2022, and the results will be ready for publication by September 2025. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D.
Conclusions: This study will provide the first large scale examination variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction.
Trial Registration: Clinical Trials Registry-India CTRI/2022/02/040619; https://tinyurl.com/mrywf6bf
International Registered Report Identifier (IRRID): DERR1-10.2196/59308
doi:10.2196/59308
Keywords
Introduction
Type 2 diabetes mellitus (T2D) is the leading cause of chronic kidney disease, end-stage renal disease, blindness, and nontraumatic amputation; it also substantially increases the risk of myocardial infarction, stroke, and heart failure [Pratley RE. The early treatment of type 2 diabetes. Am J Med. 2013;126(9 Suppl 1):S2-S9. [CrossRef] [Medline]1]. Its prevalence is particularly high in India, which is now the most populous country in the world. As of 2023, more than 100 million people living in India have diabetes, representing more than 11% of the population and an additional 136 million have prediabetes [Anjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, et al. ICMR-INDIAB Collaborative Study Group. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). Lancet Diabetes Endocrinol. 2023;11(7):474-489. [CrossRef] [Medline]2]. These numbers are anticipated to continue to grow rapidly. The lifetime risk of T2D among 20-year-olds who are obese and living in India is estimated to be more than 86% [Luhar S, Kondal D, Jones R, Anjana RM, Patel SA, Kinra S, et al. Lifetime risk of diabetes in metropolitan cities in India. Diabetologia. 2021;64(3):521-529. [FREE Full text] [CrossRef] [Medline]3]. The rising prevalence of this condition in India is believed to be the result of changing diets, increasingly sedentary occupations, lower levels of physical activity in the context of urbanization, and rapidly increasing rates of obesity [India State-Level Disease Burden Initiative Diabetes Collaborators. The increasing burden of diabetes and variations among the states of India: the global burden of disease study 1990-2016. Lancet Glob Health. 2018;6(12):e1352-e1362. [FREE Full text] [CrossRef] [Medline]4].
These trends are particularly concerning because of important differences between the presentation and consequences in T2D among individuals of South Asian origin compared with other racial and ethnic groups. These differences, which often referred to as the “Indian Phenotype” or “South Asian Phenotype” [Patel SA, Shivashankar R, Ali MK, Anjana R, Deepa M, Kapoor D, et al. CARRS Investigators. Is the "South Asian Phenotype" unique to South Asians?: comparing cardiometabolic risk factors in the CARRS and NHANES studies. Glob Heart. 2016;11(1):89-96.e3. [FREE Full text] [CrossRef] [Medline]5-Sattar N, Gill JMR. Type 2 diabetes in migrant south Asians: mechanisms, mitigation, and management. Lancet Diabetes Endocrinol. 2015;3(12):1004-1016. [FREE Full text] [CrossRef] [Medline]7], are characterized by the onset of T2D at a younger age and substantially lower BMI than people of other races and ethnicities [Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. Diabetes Epidemiology Study Group in India (DESI). High prevalence of diabetes and impaired glucose tolerance in India: national urban diabetes survey. Diabetologia. 2001;44(9):1094-1101. [CrossRef] [Medline]8-Hsu WC, Araneta MRG, Kanaya AM, Chiang JL, Fujimoto W. BMI cut points to identify at-risk Asian Americans for type 2 diabetes screening. Diabetes Care. 2015;38(1):150-158. [FREE Full text] [CrossRef] [Medline]10]. Individuals of Indian origin have higher levels of insulin resistance (and for longer periods of time) and premature beta-cell failure [Sattar N, Gill JMR. Type 2 diabetes in migrant south Asians: mechanisms, mitigation, and management. Lancet Diabetes Endocrinol. 2015;3(12):1004-1016. [FREE Full text] [CrossRef] [Medline]7]. They are more likely to develop the fatal complications of T2D, most notably heart disease [Sattar N, Gill JMR. Type 2 diabetes in migrant south Asians: mechanisms, mitigation, and management. Lancet Diabetes Endocrinol. 2015;3(12):1004-1016. [FREE Full text] [CrossRef] [Medline]7]. These features are thought to result from a mix of lifestyle, epigenetics, and fetal programming factors [Sattar N, Gill JMR. Type 2 diabetes in migrant south Asians: mechanisms, mitigation, and management. Lancet Diabetes Endocrinol. 2015;3(12):1004-1016. [FREE Full text] [CrossRef] [Medline]7,Unnikrishnan R, Gupta PK, Mohan V. Diabetes in South Asians: phenotype, clinical presentation, and natural history. Curr Diab Rep. 2018;18(6):30. [CrossRef] [Medline]11,Gujral UP, Pradeepa R, Weber MB, Narayan KMV, Mohan V. Type 2 diabetes in South Asians: similarities and differences with white Caucasian and other populations. Ann N Y Acad Sci. 2013;1281(1):51-63. [FREE Full text] [CrossRef] [Medline]12].
The fundamental goal of diabetes management is to maintain near-normal glucose levels. A variety of self-management behaviors, in particular adherence to diet and regular exercise, are central to this goal. An extensive body of evidence demonstrates that aiding patients with T2D with self-management behaviors is associated with improvements in a wide range of outcomes including knowledge, self-care behaviors, weight, quality of life, hemoglobin A1c (HbA1c), all-cause mortality, and health care costs [Powers MA, Bardsley J, Cypress M, Duker P, Funnell MM, Fischl AH, et al. Diabetes self-management education and support in type 2 diabetes. Diabetes Educ. 2017;43(1):40-53. [CrossRef] [Medline]13,American Diabetes Association. 5. Facilitating behavior change and well-being to improve health outcomes:. Diabetes Care. 2021;44(Suppl 1):S53-S72. [CrossRef] [Medline]14].
Guidelines recommend that nutritional guidance be personalized based on nutritional status, lifestyle, and metabolic goals [ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. on behalf of the American Diabetes Association. 5. Facilitating positive health behaviors and well-being to improve health outcomes: standards of care in diabetes-2023. Diabetes Care. 2023;46(Supple 1):S68-S96. [FREE Full text] [CrossRef] [Medline]15]. Despite this, most dietary advice for individuals with T2D remains generic emphasizing reductions in calories and minimization of carbohydrates [Anjana RM, Srinivasan S, Sudha V, Joshi SR, Saboo B, Tandon N, et al. ICMR-INDIAB Collaborative Study Group. Macronutrient recommendations for remission and prevention of diabetes in Asian Indians based on a data-driven optimization model: the ICMR-INDIAB national study. Diabetes Care. 2022:dc220627. [CrossRef] [Medline]16]. However, there are marked interindividual responses in postprandial glucose response (PPGR) [Vega-López S, Ausman LM, Griffith JL, Lichtenstein AH. Interindividual variability and intra-individual reproducibility of glycemic index values for commercial white bread. Diabetes Care. 2007;30(6):1412-1417. [CrossRef] [Medline]17]. A study conducted in Israel found substantial PPGR variability to standardized meals for individuals without diabetes [Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. [FREE Full text] [CrossRef] [Medline]18]. Similar data has been generated in the United Kingdom, the United States, and China [Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021;27(2):321-332. [FREE Full text] [CrossRef] [Medline]19-Ma Y, Fu Y, Tian Y, Gou W, Miao Z, Yang M, et al. Individual postprandial glycemic responses to diet in n-of-1 trials: westlake N-of-1 trials for macronutrient intake (WE-MACNUTR). J Nutr. 2021;151(10):3158-3167. [FREE Full text] [CrossRef] [Medline]21].
There have been no studies characterizing food responsiveness among individuals living in India and virtually no published data, from any judication, in the variability in PPGR for individuals with T2D [Dalal P, Yazdani M, Snyder M, Rahili S, Torbaghan S. 75-LB: machine-learned, biophysical prediction of glucose response for 1,000 subjects. Diabetes. 2020;69((Supplement_1)):75-LB. [CrossRef]22]. Given the unique Indian diabetes phenotype and differences between Indian and western diets, in specific much higher rates of carbohydrate consumption overall [Hosseini F, Jayedi A, Khan TA, Shab-Bidar S. Dietary carbohydrate and the risk of type 2 diabetes: an updated systematic review and dose-response meta-analysis of prospective cohort studies. Sci Rep. 2022;12(1):2491. [FREE Full text] [CrossRef] [Medline]23] and the centrality of white rice and refined wheat [Radhika G, Sathya RM, Ganesan A, Saroja R, Vijayalakshmi P, Sudha V, et al. Dietary profile of urban adult population in South India in the context of chronic disease epidemiology (CURES-68). Public Health Nutr. 2011;14(4):591-598. [CrossRef] [Medline]24], there are very likely to be differences in blood glucose responses to food and exercise in India than observed elsewhere, just as there have been in Indians’ responses to diabetes medications [Singh AK. Incretin response in Asian type 2 diabetes: are Indians different? Indian J Endocrinol Metab. 2015;19(1):30-38. [FREE Full text] [CrossRef] [Medline]25]. Accordingly, the goal of this study is to characterize and identify factors associated the variability in PPGR among individuals with T2D in India.
Methods
This prospective cohort study seeks to evaluate the relationship between PPGR and self-management activities including diet, exercise, and other daily routines, for individuals with T2D in India (Figure 1).
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Study Setting
This trial is being conducted at 14 outpatient clinics in geographically distinct regions across India with population sizes ranging from 1.2 to 34 million. Sites were identified and managed by IQVIA, a multinational contract research organization, and were included if they specialized in the care of individuals with diabetes, had an established research infrastructure for the conduct of diabetes-related studies including a site principal investigator who is a diabetologist (with clinical training in endocrinology or internal medicine), a local ethics committee to provide study oversight, and a sufficient volume of potentially eligible patients. Study enrollment began in May 2022.
Eligible Participants and Enrollment
The study population consists of adults with diabetes and suboptimal disease control, classified as HbA1c ≥7. Complete inclusion and exclusion criteria are summarized in Textbox 1.
Inclusion criteria
- Age ≥ 18 and <75 years.
- Physician-diagnosed type 2 diabetes treated with ≥1 oral hypoglycemic agents
- Hemoglobin A1c≥ 7.0% recorded within the past 30 days.
- Mobile phone capable of running protocol-specified apps.
- Functional English literacy.
Exclusion criteria
- Unable or unwilling to provide informed consent or comply with the study-specified procedures.
- Current use of prandial insulin including a continuous insulin infusion pump.
- Currently pregnant or planning to become pregnant.
- Estimated life expectancy of ≤12 months.
- Active cancer.
- Myocardial infarction or stroke in the last 6 months.
- Receiving or planned to initiate dialysis for end-stage renal disease.
- Receiving oral or intravenous steroids.
- Any contraindication to using a continuous glucose monitor.
Potentially eligible patients are identified from clinic records and are invited to attend an in-person screening visit at which time eligibility is confirmed and written informed consent obtained. Consenting patients are asked to provide sociodemographic and medical information (specifically, age, sex, predominant diet, health conditions, family history, and current medications) and to complete baseline surveys including the World Health Organization’s STEPwise Approach to Non-Communicable Disease Risk Factor Surveillance (STEPS) survey [STEPwise approach to NCD risk factor surveillance (STEPS). World Health Organization URL: https://www.who.int/teams/noncommunicable-diseases/surveillance/systems-tools/steps [accessed 2024-10-21] 26], World Health Organization-Five Well-Being Index (WHO-5) [Topp CW, Østergaard SD, Søndergaard S, Bech P. The WHO-5 well-being index: a systematic review of the literature. Psychother Psychosom. 2015;84(3):167-176. [FREE Full text] [CrossRef] [Medline]27]. Diabetes Distress Scale [Polonsky WH, Fisher L, Earles J, Dudl RJ, Lees J, Mullan J, et al. Assessing psychosocial distress in diabetes: development of the diabetes distress scale. Diabetes Care. 2005;28(3):626-631. [CrossRef] [Medline]28], Wilson Adherence Scale [Wilson IB, Lee Y, Michaud J, Fowler FJ, Rogers WH. Validation of a new three-item self-report measure for medication adherence. AIDS Behav. 2016;20(11):2700-2708. [FREE Full text] [CrossRef] [Medline]29], and the Pittsburgh Sleep Quality Index [Buysse DJ, Berman SR, Kupfer DJ, Monk TH, Reynolds CF. Pittsburgh Sleep Quality Index (PSQI). University of Pittsburgh; 1989. URL: https://eprovide.mapi-trust.org/instruments/pittsburgh-sleep-quality-index#basic_description [accessed 2024-10-21] 30].
Biometric data including blood pressure, heart rate, weight, height, and body measurements at the upper arm, thigh, calf, waist, and hips, are collected by study coordinators at each site. Finally, enrolled participants provide blood samples including a complete blood count, HbA1c, blood electrolytes, creatinine, cholesterol, as well as urinalysis.
After completing baseline assessments, participants are fitted with an Abbott Freestyle Libre continuous glucose monitors (CGM) sensor on their upper, nondominant arm and are provided with a Xiaomi Mi Band Smart Wristband (heart rate monitor) and a Roche Accu-Chek glucometer with testing supplies, and dietary supplements to be consumed with their standardized meals (refer to Follow-up Procedures section). Study-specific apps are downloaded on to the participants’ smartphones to allow them to log dietary intake and synchronize their continuous glucose and heart rate monitors. As a back-up, participants are given a paper dietary logbook and a Freestyle Libre CGM reader with which to collect protocol-specified data.
Follow-Up Procedures
Participants are followed for 14 days. They are instructed to wear the CGM and heart rate monitor. The heart rate monitor is to be always worn, including during sleep, and only removed for recharging. Participants are also asked to check their capillary glucose on days 2 through 6 before breakfast and dinner.
Participants log their full dietary intake using the study app or logbook over the 14-day study period, including all standardized test meals and free-living foods (including snacks), beverages (including water), and medications. Participants also log all exercise.
Participants are required to consume protocol-specified meals and to perform light activity, as described in Table 1. The standardized meals consist of vegetarian breakfast foods which participants are to prepare in their homes.
The meals vary in their proportion of carbohydrate, fiber, protein, and fat (refer to Table 2).
Day and timing | Meal type | ||
1 | |||
Breakfast | Fasting | ||
Lunch | As desired | ||
Dinner | As desired | ||
2 | |||
Breakfast | Perceived healthy meal | ||
Lunch | Post meal exercise | ||
Dinner | Typical mixed protein with dinner | ||
3 | |||
Breakfast | Fiber supplement with standardized test meal | ||
Lunch | Premeal exercise | ||
Dinner | Alternative mixed protein with dinner | ||
4 | |||
Breakfast | Fiber supplement before standardized test meal | ||
Lunch | As desired | ||
Dinner | As desired | ||
5 | |||
Breakfast | Protein supplement with standardized test meal | ||
Lunch | As desired | ||
Dinner | Alternative mixed protein with dinner | ||
6 | |||
Breakfast | Protein supplement before standardized test meal | ||
Lunch | Added fiber with lunch | ||
Dinner | Alternative mixed protein with added protein | ||
7 | |||
Breakfast | Regular breakfast | ||
Lunch | Added fruit | ||
Dinner | Cooled carbohydrate | ||
8 | |||
Breakfast | Regular breakfast with added protein | ||
Lunch | Added fruit with protein | ||
Dinner | Regular carbohydrate | ||
9 | |||
Breakfast | Protein followed by carbohydrate | ||
Lunch | Water 30 minutes before lunch | ||
Dinner | As desired | ||
10 | |||
Breakfast | Standardized test meal | ||
Lunch | As desired including dessert | ||
Dinner | Protein followed by carbohydrates and vegetables | ||
11 | |||
Breakfast | Standardized test meal with postmeal exercise | ||
Lunch | As desired including desert and cinnamon | ||
Dinner | Vegetables followed by protein then carbohydrates | ||
12 | |||
Breakfast | Standardized test meal with postmeal exercise | ||
Lunch | As desired | ||
Dinner | As desired finishing eating by 8 PM | ||
13 | |||
Breakfast | Water before late breakfast + postmeal exercise | ||
Lunch | Low glycemic index lunch | ||
Dinner | Low glycemic index dinner + finish dinner by 8 PM + exercise after meal | ||
14 | |||
Breakfast | Water before late breakfast + exercise after meal | ||
Lunch | Low glycemic index lunch + exercise after meal | ||
Dinner | Low glycemic index dinner + finish dinner by 8 PM + exercise after meal |
Meal characteristics | Meal type | ||
Carbohydrate | Added fiber | Added protein | |
Example meal | 2 aloo parathas with curd | 2 aloo parathas with curd + fiber supplement | 2 aloo parathas with curd + protein supplement |
Energy (kcal) | 474.4 | 506.4 | 594.4 |
Carbohydrate (g) | 61.5 | 64.1 | 62.9 |
Fat (g) | 12.5 | 12.5 | 14.6 |
Protein (g) | 7.7 | 7.8 | 31.9 |
Fiber (g) | 8.3 | 21.9 | 8.3 |
Carbohydrate (% energy) | 51.9 | 50.6 | 42.3 |
Fat (% energy) | 23.8 | 22.3 | 22.1 |
Protein (% energy) | 6.46 | 6.05 | 21.4 |
Participants are instructed to fast for a minimum of 8 hours before and 3 hours after consuming the standardized breakfast meal; during these fasting periods, limit exercise and drink only still (not sparkling) water, tea, or coffee in moderation; and eat the meal, in its entirety, within 20 minutes. After completing the postmeal fasting period on standardized test meal days, participants may consume other foods as they normally would unless there are other meal modifications specified by the protocol later the same day.
On other days, participants are asked to consume normal foods with protocol-specified constraints. For example, on different days, participants vary the types of mixed protein (eg, different types of lentils with or without added protein), ordering with the type of foods consumed (eg, protein before carbohydrate vs protein with carbohydrate), consume water before their meal, go for a walk after eating, or eat what they perceive to be a healthy meal. Where applicable, participants are given several options as to which of their usual foods are acceptable for each protocol specified food modification.
During the follow-up period, participants are contacted by phone and text messages to ensure protocol compliance. For participants using the study-specific smartphone apps, a web dashboard is used to monitor the completeness of dietary logging and CGM scanning, with outreach to participants initiated when missing data is detected. Participants using paper logs and a CGM reader, and therefore for whom no dashboard information is available, are contacted daily to ask about protocol compliance. Ad hoc midstudy visits are used to further ensure accurate data collection. If the outreach identifies a problem with a CGM (ie, it was damaged, fell off, or malfunctioned), study staff provide a replacement within 24 hours during which time participants are asked to pause their meal protocol and restart once their CGM has been reapplied and recalibrated. If a test meal was not consumed as intended, participants are provided with the option to repeat the meal.
On day 15, participants are asked to remove their CGM. If need be, study staff help record or correct missing or inaccurate food, activity, and medication data.
Statistical Analysis Plan
The study’s primary outcome is PPGR. Following the Wolever and Jenkins method [Wolever TM, Jenkins DJ. The use of the glycemic index in predicting the blood glucose response to mixed meals. Am J Clin Nutr. 1986;43(1):167-172. [CrossRef] [Medline]31], as adapted by Zeevi et al [Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. [FREE Full text] [CrossRef] [Medline]18], Mendes-Soares et al [Mendes-Soares H, Raveh-Sadka T, Azulay S, Edens K, Ben-Shlomo Y, Cohen Y, et al. Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open. 2019;2(2):e188102. [FREE Full text] [CrossRef] [Medline]20], and Berry et al [Berry SE, Valdes AM, Drew DA, Asnicar F, Mazidi M, Wolf J, et al. Human postprandial responses to food and potential for precision nutrition. Nat Med. 2020;26(6):964-973. [FREE Full text] [CrossRef] [Medline]32], logged meal times and continuous glucose measurements will be used to calculate the incremental area under the curve. Before conducting analyses, meals logged less than 30 minutes apart will be merged and meals logged within 90 minutes of other meals will be removed. Meals that are very small (<15 g and <70 calories), very large (>1 kg), with implausibly low PPGR values (ie, a PPGR < 5 mg/dL·h after consuming ≥ 40 g of carbohydrates), that are incompletely logged, and which are consumed in the first and last 12 hours of the CGM connection will also be removed. To reduce noise, the median of all glucose values from the 30-minute period before the meal will be taken as the initial glucose level, above which the incremental area will be calculated. Meals that had incomplete glucose measurements in the time window of 30 minutes before and 2 hours after the logged mealtime will be filtered out.
Descriptive statistics will be used to plot the range of PPGRs responses to standardized test meals as well as the correlation between PPGR and the nutritional composition of the logged meals (ie, carbohydrates, fat, and protein).
A machine learning predictor will be developed based on stochastic gradient boosting regression (XGBoost, version 2.2.1; The XG Boost Contributors) [Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Association for Computing Machinery; 2016. Presented at: KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 13-17, 2016:785-794; California, San Francisco, USA. [CrossRef]33] using the XGBRegressor class. PPGR will be predicted as the sum of predictions from thousands of decision trees. Trees will be inferred sequentially, with each trained on the residual of all previous trees. The features incorporated in each tree are selected by an inference procedure from features representing meal content (ie, calorie, protein, carbohydrate, and fiber content), meal timing, baseline demographics (ie, age, sex, predominant diet, health conditions, family history, and current medications), baseline survey results (ie, WHO STEPS, WHO-5, Diabetes Distress School, Wilson Adherence Scale, and Pittsburgh Sleep Quality), baseline biometric values (ie, blood pressure, heart rate, weight, height, and body measurements at the upper arm, thigh, calf, waist, and hips), baseline laboratory values (complete blood count, HbA1c, blood electrolytes, creatinine, cholesterol, and urinalysis), as well as CGM, heart rate, and activity data.
Performance will be assessed by holding out 30% of the sample and using 5-fold cross-validation in which cross-validation participants are divided into 5 groups, the model will be trained on the other 4 parts. Random datasets of the same size as the original will be sampled with replacement from the original dataset, and the entire training and validation process will be repeated. The performance will be measured by the ability to accurately predict meals reported by the held-out participants. Prediction results will be aggregated, and Pearson product moment correlation with the measured PPGRs will be reported. The SE for the calculated performance will be assessed using at least a thousand iterations of bootstrapping until the errors stabilize. Model discrimination will be assessed using a binary cut-point for PPGR, set at the 50th percentile of all observed PPGR values, plotting a receiver operating characteristic curve (ROC) and then calculating the area under the ROC.
Sample Size Considerations
A total of 1050 individuals will be targeted for recruitment. Assuming a 5% loss to follow-up, this corresponds to 1000 evaluable individuals at the end of the study. The study has been designed to predict postprandial glucose responses based on individual characteristics and 1000 participants followed for 14-days will result more than 4 million glucose readings (assuming glucose readings from the CGMs every 5 minutes) and 42,000 meals (assuming 3 meals per participant per day). This volume of data will also provide more than 80% power to detect correlations of a magnitude of r=0.13 (R2=0.02) with P<.005. We will also be sufficiently powered to detect effects of r=0.17 (R2=0.03, ie, explaining 2.7% of interindividual variation) with P<.001, that is, accounting for 5000 independent hypothesis tests.
Ethical Considerations
This study was approved by the following ethics committees at all institutions enrolling patients: SShrey Hospital Institutional Ethics Committee (DHINDIA_2021_01A04), Inamdar Multispecialty Hospital Ethics Committee (DHINDIA_2021_01A04), Neelima Hospitals Institutional Ethics Committee (DHINDIA_2021_01A04), Jaipur National University Institutional Ethics Committee (JNUIMSRC/IEC/2022/06), Maharaja Agrasen Hospital Institutional Ethics Committee EP/F-174), Ganesh Shankar Vidyarthi Memorial Medical College Ethics Committee (EC/72/March/2022), Mar Augustine Golden Jubilee Hospital Institutional Ethics Committee (DHINDIA_2021_01A04), Medisys Clinisearch Ethical Review Board, Dayanand Medical College and Hospital Drug Trials Ethics Committee (DMCH/DTEC/2020/1242), Chennai Meenakshi Multispecialty Hospital Ethics Committee (CMMHEC/22/02), Sparsh Hospital Institutional Ethics Committee (ZZA78309), Medical College of Kolkata Institutional Ethics Committee (MC/KOL/IEC/SPON/1296/03/22), Ethics Committee Downtown Hospital, and CHL-Hospitals Integrity Ethics Committee.
Results
Data collection commenced in May 2022, and the results will be ready for publication by October 2025. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D.
Discussion
Study Implications
This study will provide the first large scale examination of variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction. We hypothesize that there will be substantial interindividual variability in PPGR and that, based on the data collected from this study, machine learning models will be able to accurately predict individual PPGR responses. This will facilitate the prescription of truly personalized diets for individuals with T2D.
These results will be particularly important in the context of the rapidly rising prevalence of T2D in India [India. International Diabetes Federation URL: https://idf.org/our-network/regions-members/south-east-asia/members/94-india.html [accessed 2024-10-21] 34]. Along with medications and physical activity, diet is a key tenant of effective blood glucose control [ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. on behalf of the American Diabetes Association. 5. Facilitating positive health behaviors and well-being to improve health outcomes: standards of care in diabetes-2023. Diabetes Care. 2023;46(Supple 1):S68-S96. [FREE Full text] [CrossRef] [Medline]15]. Like in other jurisdictions, guidelines call for individualization of meal planning, which is sometimes referred to as “Medical Nutritional Therapy.” Despite this, personalization of dietary plans are generally based upon broad constructs like age, activity level, health status, and preferences, and, for all patients, tend to emphasize overall calorie reductions and minimization of carbohydrate [Anjana RM, Srinivasan S, Sudha V, Joshi SR, Saboo B, Tandon N, et al. ICMR-INDIAB Collaborative Study Group. Macronutrient recommendations for remission and prevention of diabetes in Asian Indians based on a data-driven optimization model: the ICMR-INDIAB national study. Diabetes Care. 2022:dc220627. [CrossRef] [Medline]16]. especially added sugars and refined grains, in favor of the consumption of nonstarchy vegetables and foods that are high in protein [Evert AB, Dennison M, Gardner CD, Garvey WT, Lau KHK, MacLeod J, et al. Nutrition therapy for adults with diabetes or prediabetes: a consensus report. Diabetes Care. 2019;42(5):731-754. [FREE Full text] [CrossRef] [Medline]35].
However, emerging data demonstrates that there are marked interindividual responses to food [Vega-López S, Ausman LM, Griffith JL, Lichtenstein AH. Interindividual variability and intra-individual reproducibility of glycemic index values for commercial white bread. Diabetes Care. 2007;30(6):1412-1417. [CrossRef] [Medline]17], attributed to differences in physical activity [Dunstan DW, Kingwell BA, Larsen R, Healy GN, Cerin E, Hamilton MT, et al. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care. 2012;35(5):976-983. [FREE Full text] [CrossRef] [Medline]36], gut microbiome [Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. [FREE Full text] [CrossRef] [Medline]18,Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021;27(2):321-332. [FREE Full text] [CrossRef] [Medline]19,Søndertoft NB, Vogt JK, Arumugam M, Kristensen M, Gøbel RJ, Fan Y, et al. The intestinal microbiome is a co-determinant of the postprandial plasma glucose response. PLoS One. 2020;15(9):e0238648. [FREE Full text] [CrossRef] [Medline]37], and genetics [Carpenter D, Dhar S, Mitchell LM, Fu B, Tyson J, Shwan NAA, et al. Obesity, starch digestion and amylase: association between copy number variants at human salivary (AMY1) and pancreatic (AMY2) amylase genes. Hum Mol Genet. 2015;24(12):3472-3480. [FREE Full text] [CrossRef] [Medline]38], including in variations in skeletal glucose transporters related to insulin resistance [Gibbs EM, Stock JL, McCoid SC, Stukenbrok HA, Pessin JE, Stevenson RW, et al. Glycemic improvement in diabetic db/db mice by overexpression of the human insulin-regulatable glucose transporter (GLUT4). J Clin Invest. 1995;95(4):1512-1518. [CrossRef] [Medline]39]. For example, a study conducted in the United States among nondiabetic individuals with a mean BMI of 27 found PPGR to a standardized meal of bagel and cream cheese ranged from 6 to 94 mg/dL·h [Mendes-Soares H, Raveh-Sadka T, Azulay S, Edens K, Ben-Shlomo Y, Cohen Y, et al. Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open. 2019;2(2):e188102. [FREE Full text] [CrossRef] [Medline]20]. A similar study conducted in Israel enrolled nondiabetic individuals of whom 3-quarters had a BMI ≥ 25 and found mean PPGR to bread and butter of 44 mg/dL·h but the bottom decile had responses of ≤ 15 mg/dL·h and the top decile has responses ≥ 79 mg/dL·h [Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. [FREE Full text] [CrossRef] [Medline]18]. Similar data have been generated for individual without diabetes in the United Kingdom and the United States [Asnicar F, Berry SE, Valdes AM, Nguyen LH, Piccinno G, Drew DA, et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med. 2021;27(2):321-332. [FREE Full text] [CrossRef] [Medline]19,Mendes-Soares H, Raveh-Sadka T, Azulay S, Edens K, Ben-Shlomo Y, Cohen Y, et al. Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open. 2019;2(2):e188102. [FREE Full text] [CrossRef] [Medline]20], and for individuals with type 1 diabetes in Israel [Shilo S, Godneva A, Rachmiel M, Korem T, Kolobkov D, Karady T, et al. Prediction of personal glycemic responses to food for individuals with type 1 diabetes through integration of clinical and microbial data. Diabetes Care. 2022;45(3):502-511. [CrossRef] [Medline]40].
There is exceptionally limited data for variability in PPGR for individuals with T2D in the peer-reviewed although it is highly likely that such variability exists [Dalal P, Yazdani M, Snyder M, Rahili S, Torbaghan S. 75-LB: machine-learned, biophysical prediction of glucose response for 1,000 subjects. Diabetes. 2020;69((Supplement_1)):75-LB. [CrossRef]22]. The primary goal of our study is to fill this void and to generate an India-specific machine-learning models on the basis of which PPGR can be predicted with high accuracy for T2D. Similar models have been built in other jurisdictions. For example, a machine learning algorithm trained on CGM data, dietary, activity, anthropometrics, and gut microbiota for nondiabetic individuals in Israel was much more accurate at predicting PPGR than generic models based on the carbohydrate content or the amount of calories in a meal [Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015;163(5):1079-1094. [FREE Full text] [CrossRef] [Medline]18]. A separate US-based study had similar findings [Mendes-Soares H, Raveh-Sadka T, Azulay S, Edens K, Ben-Shlomo Y, Cohen Y, et al. Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw Open. 2019;2(2):e188102. [FREE Full text] [CrossRef] [Medline]20].
Among individuals with diabetes, a study in the Netherlands that included a small number of individuals with T2D along with individuals with prediabetes and normal glucose metabolism, a machine learning model based on CGM data was highly accurate in predicting future glucose values but this study did not specifically evaluate the ability to predict PPGR [van Doorn WPTM, Foreman YD, Schaper NC, Savelberg HHCM, Koster A, van der Kallen CJH, et al. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: the Maastricht study. PLoS One. 2021;16(6):e0253125. [FREE Full text] [CrossRef] [Medline]41]. A US study of 1000 patients of whom 1-quarter had T2D found that a machine learning model trained on CGM, HRM data and food logs was highly accurate at predicting PPGR but this study has, to our knowledge, only been published in abstract form [Dalal P, Yazdani M, Snyder M, Rahili S, Torbaghan S. 75-LB: machine-learned, biophysical prediction of glucose response for 1,000 subjects. Diabetes. 2020;69((Supplement_1)):75-LB. [CrossRef]22]. These studies have all relied on CGM data to make their predictions. While these devices are increasingly used, practice guidelines do not recommend their long-term use for most individuals with T2D [American Diabetes Association Professional Practice Committee. 7. Diabetes technology: standards of care in diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S126-S144. [CrossRef] [Medline]42]. Accordingly, a key goal of our study will be to explore the ability to predict PPGR response without reliance on CGM data or with very limited blood glucose data from patients.
Limitations
There are several limitations to our approach. Our approach is purposely pragmatic and is intended to simulate real-world circumstances for individuals with T2D living in India. Similar to studies conducted in other jurisdictions, we rely on self-reported dietary and activity information. And, while we are auditing patient logs on an ongoing basis, there may nevertheless be issues with protocol adherence that may undermine the accuracy of the data we collect. Participants are being recruited from clinics, predominantly caring for individuals with diabetes, are required to have functional English literacy and a cellphone capable of running study specific devices. Thus, our results may not be fully generalizable to patients who do not fulfill these criteria. Finally, some of the enrollment has overlapped with the COVID-19 pandemic, which may have influenced access to health care, dietary practices, and glucose control for individuals with T2D.
Conclusion
In conclusion, this study will provide the first large scale examination variability in blood glucose responses to food in India and will be among the first to estimate PPGR variability for individuals with T2D in any jurisdiction. Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to ultimately facilitate the prescription of truly personalized diets for individuals with T2D.
Acknowledgments
The authors wish to thank the site investigators and patients participating in the study as well as IQVIA for their assistance identifying study sites and overseeing the study conduct. This work is supported by Decipher Health, Inc.
Data Availability
The datasets generated during and analyzed during this study are not publicly available as they will be used for commercial application. Data may be available on reasonable request after executing appropriate data use and licensing agreements.
Authors' Contributions
All authors meet International Committee of Medical Journal Editors (ICMJE) criteria. NKC had overall responsibility for the study design and drafted the trial protocol and manuscript. MM is the co-principal investigator, had overall responsibility for the study design, and helped draft the study protocol and manuscript. SP and JS contributed meaningfully to study design and implementation as well as the manuscript. All authors contributed to the refinement of the study protocol and approved the final manuscript.
Conflicts of Interest
NKC and JS receive consulting fees and hold equity in Decipher Health. SP is an employee of Decipher Health. MM is an employee and holds equity in Decipher Health.
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Abbreviations
CGM: continuous glucose monitor |
HbA1c: hemoglobin A1c |
PPGR: postprandial glucose response |
ROC: receiver operating characteristic |
STEPS: STEPwise Approach to Non-Communicable Disease Risk Factor Surveillance |
T2D: type 2 diabetes mellitus |
WHO-5: World Health Organization-Five Well-Being Index |
Edited by T Leung; submitted 08.04.24; peer-reviewed by C Xiang, B Reshmi; comments to author 29.07.24; revised version received 15.09.24; accepted 27.09.24; published 23.01.25.
Copyright©Niteesh K Choudhry, Shweta Priyadarshini, Jaganath Swamy, Mridul Mehta. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 23.01.2025.
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