IntroductionDiabetes Mellitus is a chronic metabolic disorder characterized by elevated blood glucose levels due to absolute or relative insulin deficiency1. The condition includes several subtypes, such as Type 1 diabetes, Type 2 diabetes, gestational diabetes, and rarer forms linked to genetic or secondary causes. T2D is the most predominant form, with an increasing prevalence in adults and children2. The International Diabetes Federation (IDF) estimates that 537 million adults (aged 20–79 years) have been living with diabetes in 2021, a figure projected to reach 643 million by 20301. In the UAE, the age-adjusted prevalence of T2D among individuals aged 20–79 years was estimated at 16.4% by the IDF in 20213. Over the past decades, bariatric surgery has evolved into the most effective treatment for obesity and associated metabolic disorders. Beyond its well-documented role in weight loss, bariatric surgery is recognized as a powerful intervention for glycemic control in patients with T2D4.Comprehensive molecular profiling using omics technologies, including genomics, proteomics, metabolomics, and microbiome analysis, plays a crucial role in precision medicine, providing an essential approach to understanding the intricate molecular mechanisms underlying various diseases. It has been employed to identify factors linked to the progression of T2D and its associated conditions567. Recently, these technologies have been applied to explore the primary outcomes of bariatric surgery, investigate T2D biomarkers, and pave the way for novel treatment strategies for T2D. This approach aids in identifying which patients are most likely to benefit from various therapeutic interventions, including bariatric surgery. While individual studies have explored genetic, proteomic, metabolomic, or microbiome changes independently, only a few have attempted partial integration. For instance, Almby K. et al.8 examined transcriptomics and proteomics; Härm A. et al.9 and Zhao S. et al.10 focused on metabolomics and proteomics; Lau E. et al.11 investigated the gut microbiome alongside proteomics; Benton M. et al. 12 analyzed epigenomics and transcriptomics; and Vohl M-C et al. 13 integrated epigenomics, transcriptomics, and proteomics. This highlights a critical gap in the literature, underscoring the need for a comprehensive multi-omics approach to fully elucidate the molecular mechanisms underlying the metabolic effects of bariatric surgery in T2D.Therefore, the aim of this study was to investigate the impact of bariatric surgery on metabolic health, focusing on early signs of CVD, using a multi-omics approach in a cohort of patients with T2D and prediabetes in the UAE. Over a nine-month period, we prospectively collected longitudinal samples and conducted genomics analysis using whole-genome sequencing (WGS), proteomic analysis via protein immunoassays, untargeted metabolomics, and gut microbiome profiling using 16S rRNA sequencing. We developed a correlation network that revealed distinct clusters of interrelated analytes linked to physiological processes and disease progression by integrating the respective datasets. To the best of our knowledge, this study is the first to leverage a multi-omics framework to uncover the molecular mechanisms underlying metabolic improvements in T2D and prediabetes following bariatric surgery, and the first of its kind to be conducted in the Emirati population.MethodsEthics statementEthical approvals were obtained from the Department of Health – Abu Dhabi (DOH/ADHRTC/2024/1519), the Research Ethics Committee of Khalifa University of Science and Technology (H21-039), and the Cleveland Clinic Abu Dhabi Research Ethics Committee (A-2021-070). Written informed consents were obtained from all individuals after clearly explaining the study participation, which includes a follow-up period of up to nine months before any study procedures occur. The study was performed according to relevant research guidelines and committee regulations.Study design and populationA total of 19 UAE national patients, aged between 25 and 53 years, including six diagnosed with T2D and thirteen with prediabetes, were recruited from Cleveland Clinic Abu Dhabi to undergo bariatric surgery. Of these, nine received Laparoscopic Sleeve Gastrectomy (SG), five underwent Roux-en-Y Gastric Bypass (RYGB), four underwent Laparoscopic Gastric Bypass (GB), and one underwent Single Anastomosis Duodeno-Ileal Bypass with sleeve gastrectomy (SADI). Samples, clinical and demographic characteristics were collected from recruited patients at baseline (pre-surgery) and at 3-, 6-, and 9-months post-surgery (Supplementary Fig. S1). The inclusion criteria were UAE nationals (holder of a passport and family book), aged 18 to 70 years, diagnosed with T2D or prediabetes according to the American Diabetes Association criteria (T2D: Fasting Plasma Glucose (FPG) ≥ 126 mg/dL or HbA1c ≥ 6.5%; Prediabetes: FPG 100–125 mg/dL or HbA1c 5.7%–6.4%), with a family history of obesity. Participants were required to have undergone primary bariatric surgery, a BMI ≥ 30 kg/m2 with at least one obesity-related comorbidity, and the ability to undergo regular blood sampling every 3 months over a 9-month period. Individuals with hypertension are defined as having systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or taking medications for high blood pressure.DNA extractionGenomic DNA was isolated from whole blood using the QIAamp DNA Blood Mini Kit (Qiagen; Germany) according to the manufacturer’s protocol. Microbial DNA was extracted from fecal samples using the QIAamp PowerFecal Pro DNA Kit (Qiagen) following the manufacturer’s instructions.Whole-genome sequencingDNA libraries were prepared using Illumina’s DNA PCR-Free library kit (Illumina; USA). Libraries were quantified using the KAPA Library Quantification Kit (Roche; USA) on the ViiA 7 Real-Time PCR System. The WGS libraries were pooled and subsequently sequenced using Illumina’s NovaSeq 6000 system, targeting a minimum mean depth of 25X. Raw sequencing reads were demultiplexed, and the Binary Base Call (BCL) files generated by the Illumina sequencing system were converted to FASTQ format using the bcl2fastq tool (Illumina). Reads were then aligned to the human reference genome (GRCh38), and variant calling was performed using Sentieon v.202308.03 (California, USA; https://www.sentieon.com/).Protein immunoassayPlasma inflammatory markers were quantified using the Bio-Plex Pro™ Human Inflammation Panel 1 (37-plex) and Bio-Plex Pro™ Human Cytokine Panel 1 (27-plex) (Bio-Rad; USA) on the BioPlex-200 system (Bio-Rad). Additionally, serum concentrations of Myoglobin (Beckman Coulter), High-Sensitivity Troponin I (hsTnI) (Beckman Coulter), and B-type natriuretic peptide (BNP) (Beckman Coulter) were analyzed using the Access 2 Immunoassay Analyzer (Beckman Coulter), following the manufacturer’s guidelines.Untargeted metabolomics profilingMetabolomic profiling was carried out using a qualitative Quadrupole Time-of-Flight liquid chromatography-tandem mass spectrometry technique (LC-MS/MS; X500R Q-TOF; SCIEX; USA). Plasma samples were prepared using protein precipitation method with ice-cold methanol (1:4 v/v). The supernatant was filtered using a 0.22 μm Cellulose Acetate filter and transferred to HPLC amber autosampler vials. A volume of 20 μl of samples was injected into the chromatographic system. Each sample was analyzed in two analytical replicates, and two technical replicates were injected from the same sample. Six blank samples consisting of the mobile phase (A: UPLC/MS grade water (CARLO ERBA; Italy) and 0.1% formic acid (CARLO ERBA), and B: LC/MS Acetonitrile (CARLO ERBA) and 0.1% formic acid, A:B (9:1 v/v)) were analyzed in duplicates. To ensure the reproducibility of retention times and assess data quality, quality control samples were prepared by pooling 10 μl from each sample and analyzed every eighth injection. Additionally, the system was calibrated after every five samples. The separation was done using Kinetex 2.6 μm F5 Column 150 × 2.1 mm (Phenomenex) for both negative and positive models. The injection volume was 20 μl, and the column temperature was 40 °C. The mobile phase consisted of (A) UPLC/MS grade water (CARLO ERBA) and 0.1% formic acid (CARLO ERBA), and (B) LC/MS Acetonitrile (CARLO ERBA) and 0.1% formic acid. The elution program of the pump gradient was set as follows: t = 0 min, 2% B; t = 2, 2% B; t = 3, 5% B; t = 5, 80% B; t = 13, 95% B; t = 16.5, 95% B; t = 17, 2% B; t = 20, 2% B. The flow rate was set to 0.3 mL/min, and the injection needle was washed after each sample for 5 s with 25:25:25:25; water/isopropyl alcohol/methanol/acetonitrile. Turbolon Spray was used as an ion source for positive and negative modes with a voltage of 5.5 kV. The collision energy was set at 35 V, and the declustering potential was set at 60 V. To obtain a richer MS/MS spectrum, the collision energy spread was set at 15 V. The intervals of the mass scan were 50–1000 Da for both MS and MS/MS.Microbial profiling using 16S rRNA sequencingIsolated microbial DNA was PCR-amplified with 16S Amplicon PCR forward and reverse primers (Integrated DNA Technologies) targeting the V3-V4 region using 2 × KAPA HiFi HotStart ReadyMix (Roche). The primer sequences were as follows: 16S Amplicon PCR forward primer: 5’-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3’ and the 16S Amplicon PCR reverse primer: 5’-GTCTCGTGGGCTCGGAGATGTGTATAA GAGACAGGACTACHVGGGTATCTAATCC-3’. The index PCR step was implemented to attach dual indices and Illumina sequencing adapters using the Nextera XT Index Kit v2 Set A (Illumina). The libraries were run on the Illumina MiSeq system using the MiSeq Reagent Kit v3 (600 cycles) (Illumina).Statistical analysisAge differences were analyzed using a one-way ANOVA test and the comparison of sex distributions was performed using the Chi-squared test. For follow-up comparisons, the p-value was calculated using a paired t-test with a two-tailed approach.Variants from WGS data were filtered, prioritized, and classified using Franklin (Genoox; https://franklin.genoox.com/) as the primary analysis platform, following the American College of Medical Genetics and Genomics (ACMG) criteria. The initial filtering retained variants with sequencing depth ≥ 11 while excluding those that failed Franklin’s confidence score assessment. Manual verification of variants and their population frequencies was performed using the VarSome human genomic variant search engine (v12.9.0; https://varsome.com)14, which incorporates data from the Genome Aggregation Database (gnomAD v4.1). Variants located within segmental duplication regions were flagged to highlight the potential for false positives.Statistical analyses of protein immunoassay mean values were performed using GraphPad Prism 10 (https://www.graphpad.com/). Concentrations marked as “