Nutriomics and artificial intelligence nutrition obesity cohort (NAINOC): a design paper for a prospective cohort for nutrition and obesity research (original) (raw)

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Copyright © 2025 The Korean Society of Hypertension

Original Article

Minyoung Lee,1,2 Sungha Park,3,4 Soo-Hyun Park,5 Ho-Young Park,5 Yu Ra Lee,5 Min-Sun Kim,6 Miso Nam,6 Jangho Lee,5 Hyein Seo,7 Yong-ho Lee,1,2 Chan Joo Lee,4 Jae-Ho Park,5 Hye Hyun Yoo,8 Hyun-Jin Kim,9 Kyong-Oh Shin,10 Yoshikazu Uchida,10 and Kyungho Park10

Received April 29, 2025; Revised August 15, 2025; Accepted August 24, 2025.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

The increase in obesity is becoming a world-wide health issue. However, no prospective cohorts in East Asia have thoroughly explored comprehensive nutritional and multiomic data in individuals with obesity. This study is designed to establish an obesity cohort that constitutes clinical characteristics, nutritional status, laboratory profiles, metabolic complication studies, and multiomic profiles with the goal of artificial intelligence platform-based nutriomic analysis.

Methods

This study aims to enroll at least 400 obese adults (aged ≥ 19 years; body mass index ≥ 25 kg/m2) and 100 non-obese adults as controls. Obese participants have to have at least one of the following chronic metabolic diseases: hypertension, type 2 diabetes mellitus, cardiovascular disease, and metabolic syndrome. Participants will undergo assessment for demographic data, clinical, lifestyle, and dietary assessments, laboratory examination, coronary calcium/visceral fat scan, liver fibroscan, carotid ultrasound, and continuous glucose monitoring. Metabolite analysis will be conducted for blood/stool/urine/saliva samples. Deoxyribonucleic acid methylation analysis, peptidomic analysis, and lipidomic analysis will be performed on blood samples. Obese individuals will have annual study visits for collection of clinical measures and multiomics data over a 5-year period. Control individuals will have a baseline hospital visit with annual telephone follow-up for clinical event monitoring.

Conclusions

The strength of this cohort will be as follows. First, the cohort will enable the integration of nutritional intake data with other multiomics data for a comprehensive analysis. Second, inclusion of both obese individuals with various metabolic traits and non-obese individuals as controls is advantageous for studying a wide range of obesity phenotypes in comparison with non-obese conditions. Third, diverse modalities to assess metabolic and complication status will facilitate multifaceted analysis. Lastly, beyond the typical blood and stool samples in multiomic studies, the inclusion of urine, saliva, and skin samples will further refine obesity characterization.

Graphical Abstract

Keywords

Nutriomics; Multiomics; Nutrition; Obesity; Cohort

BACKGROUND

Obesity is a chronic disease marked by excess adiposity and rising at epidemic proportions, leading to heightened comorbidity and mortality risks [1]. According to the WHO, from 1975 to 2016, the prevalence of obesity has increased by 3 folds and in 2017, the prevalence of obesity in the US was reported to be 42% and by 2030, 78% of US adults are projected to be either overweight or obese [1, 2, 3]. The increase in obesity is becoming a world-wide health issue of epidemic proportion. For example, in South Korea, the prevalence of obesity, defined as body mass index (BMI) of more than 25 kg/m2, has increased from 29.7% in 2009 to 38.4% in 2021 [4]. Obesity is associated with various health problems such as type 2 diabetes mellitus (DM), dyslipidemia, hypertension, cerebrovascular disease, cancer, respiratory disorders, obstructive sleep apnea, and degenerative arthritis [1, 5, 6]. Due to higher risk of comorbidities, obesity is associated with shortening of life expectancy by 2–4 years in those with BMI of 30–35 kg/m2 and shortened life expectancy of 8–10 years in those with BMI of 40–45 kg/m2 [5]. The major cause of mortality in subjects with obesity is cardiovascular disease (CVD), which has been shown to increase progressively with increasing BMI with no significant racial difference [5, 6].

Although numerous cohort studies have demonstrated the association of obesity with many chronic diseases, there are currently no prospective cohort studies in East Asia that explored comprehensive nutritional and multiomic data in individuals with obesity. Multiomic approaches have gained significant attention for elucidating the mechanisms of chronic diseases and advancing treatment and clinical applications [7, 8]. Dietary factors have been recognized as crucial in the development of chronic diseases, and their integration into multiomic analyses has become increasingly important since environmental contributions were first suggested in 1967 [9]. Recently, nutriomic approaches that combine multiomics and nutritional evaluation have revealed novel features of cardiometabolic disease in middle-aged Europeans [10]. Based on these backgrounds, the objective of this study is to establish an obesity cohort that constitutes clinical and nutritional characteristics, blood chemistry profiles, metabolic complication studies, and multiomic profiles with the goal of nutriomic analysis [11]. We plan to obtain genomic and epigenomic data, microbiome metagenomic data, and metabolomic data from blood, feces, urine, saliva, and skin in obese and non-obese subjects. Learning-based data reduction algorithms, a subset of artificial intelligence (AI), will be used to identify genomic traits that contribute to features and comorbidities of obesity. This cohort study will afford the classification of obesity phenotypes through an AI platform, facilitating personalized dietary strategies for the treatment and prevention of obesity and frequently comorbid chronic metabolic diseases.

METHODS

This study was approved by the Institutional Review Board of Severance Hospital, Seoul, South Korea (4-2022-0645). This study will be performed as a collaborative study between the Korea Food Research Institute and Yonsei University College of Medicine, Severance hospital. This study is originally designed to follow each participant for five years after enrollment; however, extended follow-up may be considered depending on factors such as participant retention, the sustainability of the study, and the significance of the findings.

The study aims to enroll at least 400 obese adults aged ≥ 19 years with BMI ≥ 25 kg/m2 who have at least one or more chronic diseases such as hypertension, type 2 DM, and CVD. The obesity cohort is to be followed up every year for 5 years. One hundred control subjects are to be enrolled over the five-year study period for comparison with the obese subjects. Over the entire study period, the cohort will comprise 500 participants with complete dietary, clinical, and multi-omics data collected at multiple time points. The sample size was informed by prior multi-omic cohort studies and literature on sample size estimation for epigenomics and metabolomics [12, 13, 14, 15].

Inclusion criteria for individuals with obesity

1. Adults aged ≥ 19 years and with BMI ≥ 25 kg/m2

2. The subject has to have at least one of the following chronic metabolic diseases: hypertension, type 2 DM, CVD, and metabolic syndrome.

Obesity is categorized into class I (25.0–29.9), class II (30.0–34.9), and class III (≥ 35.0) according to BMI and the WHO guidelines for the Asia-Pacific region [16]. Type 2 DM is defined by either the diagnostic criteria of Korean Diabetes Association guidelines [16], a previous diagnosis by a physician, or the use of glucose-lowering drugs. Prediabetes is defined by fasting glucose levels of 100–125 mg/dL, 2-hour glucose of 140–199 mg/dL after a 75 g glucose load or meal, or glycated hemoglobin A1c (HbA1c) levels of 5.7–6.4% [16]. Definition of metabolic syndrome is based on previously established criteria [17].

Exclusion criteria for the obese group

1. Life expectancy of less than 6 months due to severe non-CVD

2. Pregnancy, suspected pregnancy, or lactation

3. Within 3 months of organ transplantation

4. Currently being treated for acute rejection of organ transplantation

5. Within 6 months of discharge after treatment for acute coronary syndrome (myocardial infarction or unstable angina)

6. Within 6 months of discharge after hospitalization for acute ischemic stroke

7. Type 1 DM

8. Currently receiving steroids or hormone replacement therapy

9. Inability to use a smartphone

Inclusion criteria for individuals without obesity as control group

Adult subjects aged ≥ 19 years with BMI between 18.5–24.9 kg/m2. The patient may have hypertension or dyslipidemia but should not have other chronic metabolic diseases such as type 2 DM and CVD. Based on the 2019–2021 Korea National Health and Nutrition Examination Survey, conducted among adults aged ≥ 19 years with a mean BMI of 23 kg/m2 representing a predominantly non-obese population, the prevalence of hypertension alone, dyslipidemia alone, and their comorbidity was 8.7%, 24.6%, and 15.0%, respectively [18]. Therefore, excluding individuals with hypertension and dyslipidemia would substantially limit the pool of eligible participants for the non-obese control group. Accordingly, hypertension and dyslipidemia were permitted as comorbid conditions in the control group. Nevertheless, the presence of hypertension and dyslipidemia in the control group may influence the risk of developing CVD and other chronic metabolic diseases within this group [19, 20]. Subgroup analyses restricted to control group participants without hypertension and dyslipidemia may be considered to account for the potential confounding effects of these conditions.

Exclusion criteria for the control group

1. Life expectancy of less than 6 months due to severe non-CVD

2. Pregnancy, suspected pregnancy, or lactation

3. Current use of physician-prescribed medications for chronic diseases other than hypertension or dyslipidemia

4. Diagnosis of malignant tumor within the past 5 years

5. Inability to use a smartphone

Cohort protocol

The research protocol for each year is summarized in Table 1.

1. Baseline demographic data: age, gender, medical history, medication history, gynecological history, and family history

2. Blood pressure (BP) measurement: after 5 minutes of rest in a sitting position, office BP will be taken in the research examination room by a trained nurse using a validated automated device (HEM 7080-IC; Omron, Kyoto, Japan). After positioning the subjects and setting the device, the average of three BP readings taken at 1-minute intervals after 5 minutes of rest will be used in this study using the nocturnal automatic BP measurement mode [21, 22].

3. Body composition using Inbody 770 (InBody Co., Seoul, Korea).

4. Questionnaire for lifestyle assessment (smoking, alcohol consumption, sleep, and physical activity): information on lifestyle factors, including smoking, alcohol consumption, sleep, and physical activity, will be collected through standardized questionnaires (Table 2).

5. Questionnaire for quality of life: quality of life will be evaluated through World Health Organization Quality of Life Brief Version (WHOQOL-BREF), which covers four domains: physical health, psychological aspects, social relationships, and environment [23].

6. Questionnaire for dietary intakes: information on subjects’ dietary intake will be collected using two methods. First, food frequency questionnaire (FFQ) specifically designed for metabolic diseases will be used to assess dietary intake over the course of one year [24]. This semi-quantitative FFQ contains 129 items in 12 categories reflecting the dietary characteristics of obese Korean adults [24]. Additionally, voluntary participants will complete another FFQ consisting of 103 items, only once. The 103-FFQ, developed as part of the Korea Genome and Epidemiology Study, encompasses a broader range of dietary categories [25]. Second, dietary intake will be collected using dietary record application for 14 days. Subjects will upload photos of the food they consume during that period via the application, and automatic recognition will collect information on the food items and their nutritional information. For subjects who have difficulty in accessing the application, paper-based food records, along with food images, will be collected instead.

7. Laboratory test: complete blood count, routine serum chemistry, HbA1c, gamma-glutamyl transferase, C-reactive protein, 25(OH) vitamin D, total vitamin D, free fatty acid, fibrinogen, adiponectin, leptin, and routine urinalysis will be measured yearly. Glucose and insulin levels for both fasting and postprandial states are tested only for patients attending the diabetes center.

8. Human-derived sample collection: blood (10 mL, EDTA tube including anticoagulant, BD, Belliver Industrial Estate, Plymouth, UK), serum (0.5 mL, cryotube aliquoted into 10 vials, AXYGEN, Corning, Shanghai, China), stool (20 g, 1 NGS sample kit, OMNIgeneGut, Genotek, Ottawa, Canada), urine (10 mL, 15 mL conical tube, SPL Life Sciences Co., Ltd, Pocheon, Korea), saliva (3 mL, 15 mL conical tube, SPL Life Sciences Co., Ltd), and skin (×3, D-Squame tape, CuDerm Corporatin, Dallas, USA) samples will be collected and stored at −70°C for future analysis.

9. Coronary calcium scan and abdominal fat computed tomography (CT): coronary artery calcium (CAC) scan will be performed with the latest generation of 256-slice CT scanner (Revolution, GE Healthcare, Waukesha, USA) for all study participants, which consists of standard scanning with 120 kV as described previously [26].

Subcutaneous fat area (SFA) and visceral fat area (VFA) is quantified using an abdominal fat CT scan (Siemens somatom sensation 64, Siemens healthcare Gmbh, Erlangen, Germany). A single 3-mm-thick axial slice at the midpoint of the L4 vertebra is obtained with participants in the supine position. Fat measurement is carried out using the TeraRecon Aquarius workstation (Aquarius iNtuition, version 4.4.6; TeraRecon, Foster City, USA). SFA is defined as adipose tissue within an attenuation range of −190 to −30 Hounsfield units, and VFA as tissue between −150 and −50 Hounsfield units. VFA refers to fat within the abdominal cavity, bounded by the abdominal and oblique muscles and the posterior surface of the vertebral body. Fat located between the musculature and skin is classified as SFA. Measurements are expressed in cm2.

10. Liver fibroscan: liver fibroscan will be conducted at the first and the fifth year according to the method described previously [27]. Fibroscan will be done using one of three models; Fibro scan 502, 530, and 630 (Echosens, Paris, France). Hepatic steatosis will be quantified using controlled attenuation parameter [28]. The presence of hepatic steatosis is defined as being ≥ S1. Hepatic fibrosis will be determined using liver stiffness value (kPa) and classified into stages F0 through F4, with stages ≥ F3 defined as advanced fibrosis [29].

11. Carotid intima-media thickness (IMT): baseline carotid IMT and plaque measurement will be performed as described previously using a high-resolution real time 8-MHz linear scanner (Acuson Juniper, Siemens Inc., Berlin, Germany) [30, 31]. Carotid plaques will be defined as focal thickening encroaching into the lumen by ≥ 50% or absolute thickness of ≥ 1.5 mm at the common carotid artery, carotid bulb, or the internal carotid artery.

12. Continuous glucose monitoring: device for continuous glucose monitoring (FreeStyle Libre®; Abbott Diabetes Care, Alameda, CA, USA) will be applied for 14 consecutive days at the second year for individuals with diabetes and at the fourth year for individuals with diabetes and prediabetes. The FreeStyle Libre Pro® sensor will be worn on the posterior upper arm in accordance with the manufacturer’s guidelines and using aseptic technique. Glucose management indicator (%), coefficient of variation, time in range (70−180 mg/dL, %), time above range (> 180 or 250 mg/dL, %), time below range (< 70 or 54 mg/dL, %) will be analyzed [32, 33].

13. Comorbidities and medication use: comorbidities and medication use will be re-evaluated at least once every year throughout the cohort follow-up period, starting from baseline enrollment. Hospitalizations and emergency room visits due to illness or accidents since the last visit, including the reason and timing of each event, will be checked every year.

Information on the presence or absence of the following diseases at baseline, as well as the age at diagnosis, will be obtained: stroke, transient ischemic attack, myocardial infarction, angina, heart failure, chronic kidney disease, hypertension, dyslipidemia, DM, thyroid disease, fatty liver disease, chronic hepatitis, liver cirrhosis, asthma or chronic obstructive pulmonary disease, osteoporosis, arthritis, autoimmune disease, and malignant tumor.

The list of medications for which usage information is collected is summarized in Table 3. For each medication, the product name, ingredient name, dosage per administration, and frequency of daily use will be recorded.

14. Cardiovascular event and chronic metabolic disease adjudication: clinical event adjudication for newly developed stroke, transient ischemic attack, myocardial infarction, angina, heart failure, chronic kidney disease, hypertension, dyslipidemia, and DM will be assessed based on medical records or in-person/telephone interview. The timing of diagnosis and related medication use, will be also identified. In medical records, the documentation of each diagnosis and the prescription history of related medications will be verified. Two investigators will review the events independently for agreement. This investigation will be conducted annually, a timeframe considered to be appropriate based on the prospective studies that captured meaningful changes in CVD incidence and associated metabolic risk factors in a yearly assessment [34, 35, 36].

15. Metabolic indices: insulin resistance is assessed using homeostatic model assessment of insulin resistance (HOMA-IR), calculated as fasting insulin [μIU/mL] × fasting glucose [mg/dL])/405 [37]. Insulin secretory pancreatic β-cell function is assessed using homeostasis model assessment of β-cell function (HOMA-β), calculated as (360 × fasting insulin [μIU/mL])/(fasting glucose [mg/dL] − 63) [37]. Insulin resistance is defined by a HOMA-IR value > 2.5 (mg/dL*μIU/mL) [38]. A HOMA-β value < 50% is considered to indicate reduced insulin secretory function [39].

16. Weight loss interventions: the current study is designed as a cohort-based observational study rather than an interventional trial, and no formal nutritional counseling or exercise programs are provided to either the obese or control group. However, participants with chronic metabolic conditions such as hypertension, type 2 DM, CVD, or metabolic syndrome may receive general recommendations or guidance regarding weight loss and lifestyle modification as part of their routine clinical care [40, 41, 42]. In addition, some individuals with obesity may pursue active weight reduction with the use of anti-obesity medications, such as orlistat, naltrexone/bupropion, phentermine/topiramate, liraglutide, and semaglutide [43]. Certain antidiabetic agents, such as sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide-1 receptor agonists, are also known to promote weight loss [44]. This study will collect data on both anti-obesity medications and weight-reducing antidiabetic drugs (Table 3). The potential impact of these weight-modifying pharmacologic interventions will be considered in subsequent analyses.

Whole blood deoxyribonucleic acid (DNA) methylation analysis

Genomic DNA samples are extracted from 500 µL of whole blood samples, and 500 µg genomic DNA is bisulfite-converted using the EZ DNA Methylation Kit (Zymo Research, Irvine, USA) according to the manufacturer’s instructions. The samples are analyzed for DNA methylation using the Infinium MethylationEPIC v2.0 BeadChip and Illumina iSan System (Illumina, Inc., San Diego, USA). Raw intensity data files (IDAT) obtained from methylation analysis are processed using the ChAMP package (ver. 2.29.1) in R 3.6.3 [45]. The champ.qc function is used to check the quality of low quality probes and samples. This includes removal of poor quality probes based on detection p-values, correction for dye bias, and evaluation of bisulfite conversion efficiency. Samples or probes that do not meet quality control (QC) thresholds are excluded from subsequent analyses. To correct for technical variations, data normalization is performed using the Beta Mixture Quantile dilation method [46] using champ.norm function in the ChAMP package. Batch effects are assessed and corrected using the champ.svd and champ.combat functions, respectively. Differential methylation analysis aims to identify differentially methylated positions and regions (DMRs) between defined sample groups. CpG sites are annotated using the manifest with mapping information for EPIC_v2 available at https://zwdzwd.github.io/InfiniumAnnotation [47]. This resource provides detailed information on the genomic context of each CpG site, including gene associations, regulatory regions, and proximity to CpG island. The champ.dmp function is used to identify individual CpG sites showing significant differences in methylation levels, employing a statistical threshold of P < 0.05 after adjustment for multiple comparisons using the false discovery rate method. For DMR identification, the champ.dmr function is applied using the Bumphunter algorithm [48]. Regions are considered differentially methylated if they contain at least three consecutive CpG sites with an average methylation difference of > 10% between groups.

Serum metabolite analysis

For the analysis of serum metabolites, 50 µL of serum sample is added to 50 µL of acetonitrile (ACN) containing an internal standard (200 µg/mL) and incubated at −20°C for 20 minutes. After extraction, centrifugation is performed at 12,000 g at 4°C for 10 minutes, and 70 µL of supernatant is diluted and mixed with 70 µL of 1% trifluoroacetic acid in water. The resulting solution is transferred to an liquid chromatography (LC) vial, and 5 µL is injected for analysis. QC samples are prepared by pooling equal volumes of all individual samples to create a representative matrix. Retention time consistency and peak intensity variation in QC injections are monitored to ensure reliable data acquisition.

All tandem mass spectrometry (MS/MS) experiments are performed using an Agilent 6470 triple quadrupole mass spectrometer (MS) instrument (Agilent, Santa Clara, USA) coupled to an Agilent 1260 infinity LC system (Agilent). A gradient eluent (A, 0.1% formic acid [FA] in water; B, 0.1% FA in ACN) is used 0.3 mL/min with ACQUITY UPLC BEH C18 column (1.7 µm, 2.1 × 100 mm) (Manchester, UK) at 40°C. The gradient elution system is controlled as follows: 0 minutes, 5% B; 0–1 minutes, 5% B; 1–5.5 minutes, 5–25% B; 5.5–7.5 minutes, 25–27% B; 7.5–8.5 minutes, 27–30% B; 8.5–10.5 minutes, 30% B; 10.5–13.5 minutes, 30–40% B; 13.5–16.5 minutes, 40–95% B; 16.5–17 minutes, 95–5% B. The gradient is finally returned to the initial condition (5% B) and held there for 5 min before running the next sample.

Fecal sample collection and microbioal 16S rRNA sequencing analysis

Fecal samples from each participant are immediately mixed with OMNIgene-GUT tubes (OMR-200; DNA Genotek, Stittsville, Canada) and shaking for a minimum of 30 seconds. And then, 250 µL of samples is placed into a 2-mL tube containing 0.3 g sterile 0.1 mm zirconia beads (BioSpec, Bartlesville, USA) with 1.2 mL ASL lysis buffer from QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) and vortexed for 1 minute. The samples are subsequently heated at 95°C for 15 minutes and then subjected to two cycles of bead beating at a frequency of 30 Hz for 1 minute using the Qiagen TissueLyser II. After centrifugation, the supernatant (1.2 mL) is treated with an InhibitEX Tablet, and 350 µL of the resulting supernatant is used in the subsequent steps with a QIAcube system (Qiagen). Library preparation of the V3–V4 hypervariable region of the 16S rRNA gene is performed according to the 16S Metagenomic Sequencing Library Preparation Illumina protocol (Part #15044223 Rev. B; Illumina). The library pool containing equal molar quantities of each sample is sequenced using the MiSeq system (Illumina), according to the manufacturer’s instructions. The QIIME software is used to assess microbial diversity, and QIIME Uclust is used to generate taxonomic composition.

Fecal metabolites analysis

The leftover fecal samples (200 µL) in the OMNIgene-GUT tubes are mixed with ACN (200 µL) and sonicated in ice bath for 10 minutes. Then, centrifugation is performed at 13,000 rpm at 4°C for 10 minutes. After centrifugation, the supernatant (50 µL), 50 µL of internal standard, and 400 µL of water are added. The solution is transferred to an LC vial, and 5 µL is injected for analysis. Also, QC samples are prepared by pooling equal volumes of all individual fecal extracts. Retention time consistency and peak intensity variation in QC injection are monitored to ensure reliable data acquisition.

All MS/MS experiments are performed using a Waters Xevo triple quadrupole MS instrument (Waters, Manchester, UK) coupled to a Waters ACQUITY H-Class LC system (Waters). A gradient eluent (A, 0.1% FA in water; B, 0.1% FA in ACN) is used 0.35 mL/min with ACQUITY UPLC BEH C18 column (1.7 µm, 2.1 × 100 mm) at 40°C. The gradient elution system is controlled as follows: 0 minutes, 5% B; 0–17.5 minutes, 5–95% B; 17.5–19 minutes, 95–89% B; 19–19.5 minutes, 89–5% B. The gradient is finally returned to the initial condition (5% B) and held there for 2.5 minutes before running the next sample.

Urine metabolite analysis

For urine samples, 400 μL is combined with 230 μL of 0.2 M sodium phosphate buffer (pH 7.0) in deuterium oxide (D2O). After centrifuging the sample at 13,000 rpm for 10 minutes at 4°C, the pH is adjusted to 7.0 ± 0.1. 540 μL of sample is combined with 60 μL of 6 mM 3-(trimethylsilyl)propionic 2,2,3,3-d4 acid in D2O. The resulting 600 μL samples are transferred to 5 mm nuclear magnetic resonace (NMR) tubes.

Urine samples are analyzed using Bruker Avance III HD 800 MHz FT-NMR Spectrometer (Bruker BioSpin, Ettlingen, Germany) equipped with a 5-mm CPTIC cryogenic probe at 298K. 1H NMR spectra using a NOESYPRESAT pulse sequence to suppress the residual water signal are collected into 64,000 data points with 64 transients, an acquisition time of 2 seconds, and a relaxation delay of 2 seconds. Phase and baseline correction of all acquired 1H NMR spectra are performed using TopSpin 3.6 and AMIX software (Bruker BioSpin), respectively. Spectral intensities are normalized by the median fold-change method to correct for systematic variation. Peaks are retained only if their QC-to-blank ratio exceeds 3 and their coefficient of variation in QC samples is ≤ 30%. Equal aliquots of all study samples are then combined to generate a pooled QC sample. Identification of metabolites is conducted using 800 MHz NMR database in Chenomx NMR Suite Version 8.6 (Chenomx Inc., Edmonton, Canada), 2D total correlation spectroscopy, and spiking experiments. Metabolite quantification is performed using the Chenomx and the metabolites concentrations are normalized to creatinine levels.

Saliva metabolite analysis

For saliva collection, all participants are instructed to fast and avoid smoking as well as dental care practices such as tooth brushing or flossing, for at least 2 hours prior to sample collection. Before collecting the samples, participants rinse their mouths with water to remove any residual debris. They then drool directly into a 15 mL conical tube until at least 3 mL of saliva is collected. The saliva samples are centrifuged at 12,000 rpm for 5 minutes at 4°C, after which 1 mL of the supernatant is transferred to a new tube for further analysis.

To 100 μL of saliva, 400 μL of ACN is combined and vortexed for 2 minutes. The mixture is centrifuged at 13,000 rpm for 15 minutes at 4°C. The supernatants are then transferred to new tube and evaporated using a nitrogen stream. The samples are stored at −80°C until the LC-MS/MS analysis, and are diluted with 100 μL of water prior to analysis.

Salivary metabolites are analyzed using a Vanquish ultra-high performance liquid chromatography system (Thermo Fisher Scientific, Santa Clara, USA) with a Hypersil GOLDTM VANQUISHTM C18 column (2.1 mm × 150 mm, 1.9 μm; Thermo Fisher Scientific) coupled to an Orbitrap Exploris 120 MS (Orbitrap MS, Thermo Fisher Scientific). The chromatographic separation analysis is conducted using a binary gradient separation with a flow rate of 0.3 mL/min at 45°C. The mobile phase consists of (A) 0.1% FA, and (B) 0.1% FA in methanol (MeOH). The acquisition methods are performed using data dependent acquisition with spray voltages of 3,500 V and −2,500 V for the positive and negative modes. The vaporizer temperature is 350°C and ion transfer tube temperature is 325°C with the auxiliary gas and sheath gas at 10 and 45 arbitrary units (A.U.), respectively. The pooled QC sample is generated using the same procedure as described for the urine sample analysis.

Peptidomic analysis

For blood peptide extraction, serum (100 μL) mixed with 100 μL of distilled water is loaded into the Oasis HLB 96-well μElution Plate, which is conditioned with 200 μL of MeOH and then equilibrated with 200 μL of distilled water. After washing with 200 μL of 5% MeOH, the peptides are eluted with 80 μL of 40% MeOH. The eluted peptide samples mixed with 10 μL of terfenadine as an internal standard are analyzed using ultra-performance liquid chromatography-quadrupole-time of flight MS (Xevo G2-S, Waters, Milford, USA). Samples are injected into an Acquity UPLC BEH C18 column (2.1 mm × 100 mm, 1.7 um; Waters). The mobile phase consists of water with 0.1% FA (A) and ACN with 0.1% FA (B) at a flow rate of 0.35 mL/min for 12 minutes. The eluted peptides are ionized by positive mode electrospray ionization and detected using Q-TOF MS using the following optimized MS conditions: 3 kV of capillary voltages, 40 V of sampling cone voltage, 800 L/h of desolvation flow rate, 400°C of desolvation temperature, and 100°C of ion source temperature. Leucine-enkephalin is used as a reference for the lock mass at a frequency of 10 seconds. A pooled QC sample—prepared by combining equal aliquots of randomly selected study samples—is run between each analysis batch. MS/MS spectra are obtained using collision energy ramps from 10 to 30 eV. Mass spectrometry data are collected from m/z 50 to 1,500 with a scan time of 0.2 seconds. The peptide sequence is analyzed by de novo peptide sequencing using BioLynx (Waters) and the peptide sequences are confirmed by BLAST sequence similarity search.

Lipidomic analysis

The total lipid extraction is performed according to the Bligh–Dyer method with minor modification. Briefly, 1.2 mL of MeOH and chloroform (1:2, v/v) with 3 μL of 10N HCl are added to 50 μL of a human plasma sample or human stratum corneum tissues stripped using adhesive tape (D-Squame tape, CuDerm Corporatin, Dallas, USA), followed by the addition of 100 pmol of each lipid as an internal standard. After vortex mixing for 10 minutes, centrifugation at 15,000 × g for 5 minutes, the lower phase is transferred to a new tube and dried under nitrogen stream. The dried samples are dissolved in MeOH and ACN (1:1, v/v). Aliquots (5 μL) are injected into a reverse-phase column (ACQUITY UPLC CSH C18 column, length 100 mm, particle size 1.7 μm, inner diameter 2.1 mm) (Waters) using a binary gradient with mobile phase A (0.2% FA with 10 mM ammonium formate) and mobile phase B (MeOH:2-propanol in 0.2% FA with 10 mM ammonium formate). The effluent from the column is directly introduced into a hybrid quadrupole linear ion trap tandem MS (API5500 QTRAP, AB/SCIEX, Framingham, USA) equipped with an ESI in positive mode or negative mode and multiple reaction monitoring (MRM). Each of lipids are detected in the MRM mode by selecting the mass to charge ratio (m/z) of each species at the precursor ion at the product ion. The following conditions are used in positive-ion MRM: ion spray voltage, 5,500 V; temperature (TEM), 600°C; curtain gas (CUR), 30 A.U.; collision gas (CAD), “Medium”; ion nebulizer gas (Gas 1 [GS1]), 50 A.U.; and auxiliary gas (Gas 2 [GS2]), 60 A.U. For negative-ion MRM, the conditions are as follows: ion spray voltage, −4,500 V; TEM, 600°C; CUR, 30 A.U.; CAD, “Medium”; GS1, 50 A.U.; and GS2, 60 A.U. Nitrogen is used as the nebulizer, curtain, and CAD. Equal volumes of each serum sample are pooled to generate a QC sample, which is injected after every ten study samples to monitor analytical stability and reproducibility. Isotopically labeled internal standards representing major lipid classes are spiked into all samples prior to extraction to correct for matrix effects and technical variability. Quantification is performed using external calibration curves prepared from authentic lipid standards at six concentration levels (r2 ≥ 0.99), with each point analyzed in duplicate. Lipid species are retained for downstream analysis if they meet the following criteria: QC-to-blank ratio ≥ 3, intra-QC coefficient of variation ≤ 15%, and consistent retention time within ± 0.15 minutes. Extraction and solvent blanks are included to exclude background contaminants. Lipid concentrations are reported as pmol/mg protein or nM, and data are processed using Analyst software (v1.7.1; Applied Biosystems, Foster City, USA) and MultiQuant (v3.0.3; SCIEX), with manual inspection of chromatographic peaks as needed.

Data collections and data cleaning

Participants in the obese group are scheduled to visit the hospital once a year from the time of enrollment until the fifth year. To minimize missed visits, research staff contact participants in accordance with the visit window, typically every 9 to 12 months, to coordinate and confirm upcoming appointments. For participants in the control group, a hospital visit is conducted only in the year of enrollment. Thereafter, annual follow-up is performed via telephone to monitor clinical events.

To encourage continued participation, several incentives are provided. When abnormal findings are detected through blood tests, functional assessments, or imaging studies, participants are offered referrals for further medical evaluation. Additionally, participants receive body composition analysis reports, enabling them to understand changes in physical and body composition parameters compared to the previous year. Furthermore, genetic reports based on single nucleotide polymorphism (SNP) analysis are distributed, providing personalized genetic risk for obesity and chronic metabolic diseases, thereby promoting awareness and engagement in personal health management.

Clinical measures and multi-omics data closest to the expected annual follow-up date, defined as 12 months from the previous year’s measurement, are selected as that year’s values. Missing values will be handled using the following approach.

Clinical variables

Clinical variables with a missing data rate below 5% are considered statistically inconsequential [49] and included in the analysis without imputation.

Multi-omics data

For metabolite analyses using serum, stool, urine, and saliva samples, as well as for DNA methylation, peptidomic, and lipidomic analyses, variables with more than 10% missingness across all samples are excluded first. Subsequently, participants with more than 10% missing values among the remaining variables are also excluded [50].

Genomic analysis with AI

DNA samples were extracted from whole blood using QIAamp DNA Blood Kits (Qiagen) according to the manufacturer’s protocol. SNP genotyping is conducted using Affymetrix Axiom KORv1.1 array (Thermo Fisher Scientific). To identify a subset of obesity-related SNPs within the cohort, both univariate and multivariate feature selection methods are applied. Initially, SNP array data from the K-chip are preprocessed using PLINK. SNPs with low minor allele frequency, high missing genotype rate, and low Hardy-Weinberg equilibrium values are filtered out to remove low-quality SNPs. Subsequently, regression-based association analysis is employed as a univariate feature selector. In this analysis, obesity-related phenotypes, such as BMI and waist circumference, are used as the independent targets, with sex and age included as covariates to remove their effects. Then, the reduced SNP data undergo multivariate feature selection. A learning-based feature selection algorithm identifies a subset of SNPs that are highly associated with obesity but not with each other. Additionally, the complexity of the selection process and the number of SNPs to be derived are important factors in this procedure.

For the classification model of diabetes based on DNA methylation status, we utilize DNA methylation array data from blood samples of obese patients and non-obese controls. During data preprocessing, the raw DNA methylation array data (.idat files) are processed using the PyMethylProcess (Version 0.1.3) Python package. This preprocessing includes normalization, removal of non-autosomal CpGs, imputation, and feature selection. After preprocessing, the dataset is divided into training (80%), validation (12.5%), and testing (7.5%) subsets. The segmented dataset is then used to build a classification model with the MethylNet (Version 0.1) Python package. First, latent space features of DNA methylation are extracted using a variational autoencoder, and a disease state prediction model is built using transfer learning. Finally, we employ SHAP to identify the CpGs important for disease state prediction and to interpret the predictions for each individual and class.

DISCUSSION

Metabolic disorders, including obesity, DM, metabolic syndrome, and CVD, are highly heterogeneous, displaying a wide range of characteristics and complexities [50]. Obesity is determined by the BMI, calculated by dividing a person’s weight in kilograms by the square of their height in meters, and categorized according to the WHO’s guidelines [51]. However, BMI fails to consider body composition, such as muscle and fat mass [50], and it also does not accurately reflect the distribution of fat, particularly excessive visceral fat [51]. Beyond the BMI itself, the accumulation of visceral/ectopic fat is considered a major contributor to cardiovascular and metabolic risk [51]. As a result, obesity categories based on BMI alone inherently show diverse risk levels for CVD, leading to the identification of phenotypes such as metabolically healthy obesity [51]. Therefore, it is crucial to classify the phenotypes of obesity and assess the cardiovascular risk for each phenotype to develop tailored strategies accordingly.

Recently, even beyond body composition and visceral adiposity, there are a wide variety of individual factors contributing to metabolic heterogeneity of obesity. Genetic and epigenetic diversity interacts differently with non-genetic factors such as health behavior and environment, leading to obesity through various etiologies [52]. In addition, alteration in the profiles of proteins belonging to metabolic pathways, oxidative stress, and inflammatory process was associated with adverse outcomes of obesity and its related diseases [53]. Individual characteristics of metabolites are thought to reflect changes in amino acid and nucleotide metabolism influenced by insulin resistance [54]. Apart from genetic background, disruption of the metabolome also predicted a higher risk of cardiovascular events than healthy metabolome [54]. Gut microbiome exhibits a large between-subjects diversity, and a disturbance in microbiota composition may induce weight gain, fat storage, and insulin resistance [55]. An individual's genomic, proteomic, metabolomic, and microbial signatures may mutually influence each other and be correlated, but each omics profile has aspects that independently contribute to the development of obesity [50, 54]. Thus, multiomics could be a suitable approach to comprehensively elucidate diverse physiological states of obesity [50].

Nutriomics is a compound word of nutrition and omics, and it is a field of multiomics that integratively analyzes the effects of food intake on the various biological data (genes, proteins, metabolites, and microbiome) [56]. The current “Nutriomics and Artificial Intelligence Nutrition Obesity Cohort (NAINOC)” study has several distinct strengths. First, the most notable strength is the comprehensive investigation of nutritional intake to analyze these data in relation to other multiomics data. Semi-quantitative FFQ used in the current study provides information on the intake frequency of 129 dish/food items, grouped according to similar recipes and main ingredients, over the past year [24]. The FFQ data can be used to estimate calorie intake and consumption of nutrients such as carbohydrate [57]. Furthermore, during the period of continuous glucose monitoring, data on dynamic changes in glucose corresponding to dietary intake will be obtained through the use of a dietary record application. This will allow to integrate real-time glucose responses to dietary intake with other multiomics data, which is unique to this study. Second, the inclusion of both obese individuals with various metabolic states and non-obese individuals as controls is another advantage in regard to classify obesity phenotypes. We included obese individuals with at least one chronic metabolic disorders other than obesity, which likely added to the metabolic diversity of the study population. Third, the use of multiple modalities to assess metabolic and complication status will facilitate multifaceted analysis. The present study not only assesses the presence or absence of metabolic disorders including type 2 DM and CVD, but also performs fibroscan to evaluate steatotic liver disease, as well as CAC scan and carotid IMT to assess atherosclerosis. Fourth, in addition to blood and stool samples frequently used in previous multiomic studies [50, 54, 58], samples of urine, saliva, and skin will further refine the individual characterization of obesity.

CONCLUSIONS

The nutriomic approach in this study was designed to classify the body's responses to food intake using various biomarkers in both obese and non-obese individuals, thereby enhancing the classification of obesity phenotypes and the development of individualized dietary treatment strategies.

Funding:This work was supported by the Main Research Program (E0210600-04: SP) of the Korea Food Research Institute, funded by the Ministry of Science and ICT.

Competing interest:S.P. received honoraria from Viatris, Organon, Boryoung, Hanmi, Daewoong, Donga, Celltrion, Servier, Daiichi Sankyo, and Handok, and a research grant from Daiichi Sankyo. S.P. has received consultation fee from Skylab. Also, S.P has received stock option from Mediwhale. Others have nothing to declare.

M.L. has received lecture honoraria from JW Pharmaceutical Corporation, Boryung Corporation, Eli Lilly and Company, Merck Sharp & Dohme, HK inno.N, Servier Korea, Handok Inc., Daewoong Pharmaceutical, KUKJE PHARM CO., LTD, GC Biopharma Corporation, Jeil Pharmaceutical Co., Ltd., Boehringer Ingelheim Korea Co., Ltd., and LG Chem, Ltd. All other authors have no conflicts to disclose; no other relationships or activities that could appear to have influenced the submitted work.

Availability of data and materials:All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Ethics approval and consent to participate:All participants provided written informed consent and the Ethics Committee of the Yonsei University College of Medicine approved this study (4-2022-0645), which conforms to the ethical principles of the 1975 Declaration of Helsinki.

Consent for publication:Not applicable.

Authors' contributions:

Park SH1, Sung-Ha Park; Park SH2, Soo-Hyun Park.

Abbreviations

A.U. arbitrary unit
ACN acetonitrile
AI artificial intelligence
BMI body mass index
BP blood pressure
CAC coronary artery calcium
CAD collision gas
CT computed tomography
CUR curtain gas
CVD cardiovascular disease
D2O deuterium oxide
DM diabetes mellitus
DMR differentially methylated region
DNA deoxyribonucleic acid
FA formic acid
FFQ food frequency questionnaire
GS1 Gas 1
GS2 Gas 2
HbA1c glycated hemoglobin A1c
HOMA-IR homeostatic model assessment of insulin resistance
HOMA-β homeostasis model assessment of β-cell function
IMT intima-media thickness
LC liquid chromatography
MeOH methanol
MRM multiple reaction monitoring
MS mass spectrometer
MS/MS tandem mass spectrometry
NAINOC Nutriomics and Artificial Intelligence Nutrition Obesity Cohort
NMR nuclear magnetic resonace
QC quality control
SFA subcutaneous fat area
SNP single nucleotide polymorphism
TEM temperature
VFA visceral fat area
WHOQOL-BREF World Health Organization Quality of Life Brief Version

Acknowledgements

We appreciate the Medical Illustration & Design (MID) team, as a member of Medical Research Support Services of Yonsei University College of Medicine, for providing excellent support with medical illustration.

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