Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus.

BACKGROUND: The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar. METHODS: Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment. RESULTS: We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel. CONCLUSION: Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.

Inflammatory protein signatures in individuals with obesity and metabolic syndrome.

There is variability in the metabolic health status among individuals presenting with obesity; some may be metabolically healthy, while others may have developed the metabolic syndrome, a cluster including insulin resistance, hypertension, dyslipidemia, and increased risk of cardiovascular disease and type 2 diabetes. The mechanisms contributing to this metabolic heterogeneity are not fully understood. To address this question, plasma samples from 48 individuals with BMI ≥ 35 kg/m2 were examined (27 with and 21 without metabolic syndrome). Fasting plasma samples were subjected to Olink proteomics analysis for 184 cardiometabolic and inflammation-enriched proteins. Data analysis showed a clear differentiation between the two groups with distinct plasma protein expression profiles. Twenty-four proteins were differentially expressed (DEPs) between the two groups. Pathways related to immune cell migration, leukocyte chemotaxis, chemokine signaling, mucosal inflammatory response, tissue repair and remodeling were enriched in the group with metabolic syndrome. Functional analysis of DEPs revealed upregulation of 15 immunological pathways. The study identifies some of the pathways that are altered and reflect metabolic health in individuals with obesity. This provides valuable insights into some of the underlying mechanisms and can lead to identification of therapeutic targets to improve metabolic health in individuals with obesity.

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities: a model development and validation study.

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities. Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37-73 years, data collected 2006-2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0-93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis. Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855-0.894 for prevalence and 0.819-0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements. Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation. Funding: University Medical Center Groningen.

Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission.

OBJECTIVE: To determine the extent to which changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials. RESEARCH DESIGN AND METHODS: We applied SomaSignal predictive tests (each derived from ∼5,000 plasma protein measurements using aptamer-based proteomics assay) to baseline and 1-year samples of trial intervention (Diabetes Remission Clinical Trial [DiRECT], n = 118, and Diabetes Intervention Accentuating Diet and Enhancing Metabolism [DIADEM-I], n = 66) and control (DiRECT, n = 144, DIADEM-I, n = 76) group participants. RESULTS: Mean (SD) weight loss in DiRECT (U.K.) and DIADEM-I (Qatar) was 10.2 (7.4) kg and 12.1 (9.5) kg, respectively, vs. 1.0 (3.7) kg and 4.0 (5.4) kg in control groups. Cardiometabolic SomaSignal test results showed significant improvement (Bonferroni-adjusted P < 0.05) in DiRECT and DIADEM-I (expressed as relative difference, intervention minus control) as follows, respectively: liver fat (-26.4%, -37.3%), glucose tolerance (-36.6%, -37.4%), body fat percentage (-8.6%, -8.7%), resting energy rate (-8.0%, -5.1%), visceral fat (-34.3%, -26.1%), and cardiorespiratory fitness (9.5%, 10.3%). Cardiovascular risk (measured with SomaSignal tests) also improved in intervention groups relative to control, but this was significant only in DiRECT (DiRECT, -44.2%, and DIADEM-I, -9.2%). However, weight loss >10 kg predicted significant reductions in cardiovascular risk, -19.1% (95% CI -33.4 to -4.91) in DiRECT and -33.4% (95% CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months. CONCLUSIONS: Intentional weight loss in recent-onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with greater (>10 kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.

Laparoscopic adjustable gastric banding with liraglutide in adults with obesity and type 2 diabetes (GLIDE): a pilot randomised placebo controlled trial.

INTRODUCTION: Obesity drives type 2 diabetes (T2DM) development. Laparoscopic adjustable gastric banding (LAGB) has lower weight reduction than other bariatric procedures. Liraglutide, a GLP-1 receptor agonist, improves weight and glycaemic control in patients with T2DM. This study aimed to determine the efficacy and safety of liraglutide 1.8 mg in participants undergoing LAGB. METHODS: GLIDE, a pilot randomised, double-blind, placebo-controlled trial, evaluated LAGB with either liraglutide 1.8 mg or placebo in participants with T2DM and obesity. Participants were randomised (1:1) to 6-months therapy post-LAGB, with further 6 months off-treatment follow-up. The primary outcome was change in HbA1c from randomisation to the end of treatment, secondary outcomes included body weight change. A sample size of 58 (29 per group) had 80% power to detect a 0.6% difference in HbA1c between groups. RESULTS: Twenty-seven participants were randomised to liraglutide (n = 13) or placebo (n = 14). Multivariate analysis showed no difference between placebo and liraglutide arms in HbA1c at 6 months (HbA1c:0.2 mmol/mol, -11.3, 11.6, p = 0.98) however, at 12 months HbA1c was significantly higher in the liraglutide arm (HbA1c:10.9 mmol/mol, 1.1, 20.6, p = 0.032). There was no difference between arms in weight at 6 months (BW:2.0 kg, -4.2, 8.1, p = 0.50), however, at 12 months weight was significantly higher in the liraglutide arm (BW:8.2 kg, 1.6, 14.9, p = 0.02). There were no significant differences in adverse events between groups. CONCLUSIONS: Our pilot data suggest no additional improvement in glycaemic control or BW with LAGB and liraglutide therapy. However, this trial was significantly underpowered to detect a significant change in the primary or secondary outcomes. Further trials are needed to investigate whether GLP-1 agonists, and particularly with more effective weekly agents (i.e. semaglutide or tirzepatide), are of benefit following metabolic surgery. CLINICAL TRIAL REGISTRATION: EudraCT number 2015-005402-11.

An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case-control study.

OBJECTIVES: To examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways. METHODS: We analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications. RESULTS: We identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome. CONCLUSIONS: The data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.

Serum proteomic profiling of rheumatoid arthritis-interstitial lung disease with a comparison to idiopathic pulmonary fibrosis.

Although interstitial lung disease (ILD) causes significant morbidity and mortality in rheumatoid arthritis (RA), it is difficult to predict the development or progression of ILD, emphasising the need for improved discovery through minimally invasive diagnostic tests. Aptamer-based proteomic profiling was used to assess 1321 proteins from 159 patients with rheumatoid arthritis with interstitial lung disease (RA-ILD), RA without ILD, idiopathic pulmonary fibrosis and healthy controls. Differential expression and gene set enrichment analyses revealed molecular signatures that are strongly associated with the presence and severity of RA-ILD and provided insight into unexplored pathways of disease. These warrant further study as non-invasive diagnostic tools and future therapeutic targets.

Proteomic biomarkers of Kleine-Levin syndrome.

STUDY OBJECTIVES: Kleine-Levin syndrome (KLS) is characterized by relapsing-remitting episodes of hypersomnia, cognitive impairment, and behavioral disturbances. We quantified cerebrospinal fluid (CSF) and serum proteins in KLS cases and controls. METHODS: SomaScan was used to profile 1133 CSF proteins in 30 KLS cases and 134 controls, while 1109 serum proteins were profiled in serum from 26 cases and 65 controls. CSF and serum proteins were both measured in seven cases. Univariate and multivariate analyses were used to find differentially expressed proteins (DEPs). Pathway and tissue enrichment analyses (TEAs) were performed on DEPs. RESULTS: Univariate analyses found 28 and 141 proteins differentially expressed in CSF and serum, respectively (false discovery rate <0.1%). Upregulated CSF proteins included IL-34, IL-27, TGF-b, IGF-1, and osteonectin, while DKK4 and vWF were downregulated. Pathway analyses revealed microglial alterations and disrupted blood-brain barrier permeability. Serum profiles show upregulation of Src-family kinases (SFKs), proteins implicated in cellular growth, motility, and activation. TEA analysis of up- and downregulated proteins revealed changes in brain proteins (p < 6 × 10-5), notably from the pons, medulla, and midbrain. A multivariate machine-learning classifier performed robustly, achieving a receiver operating curve area under the curve of 0.90 (95% confidence interval [CI] = 0.78-1.0, p = 0.0006) in CSF and 1.0 (95% CI = 1.0-1.0, p = 0.0002) in serum in validation cohorts, with some commonality across tissues, as the model trained on serum sample also discriminated CSF samples of controls versus KLS cases. CONCLUSIONS: Our study identifies proteomic KLS biomarkers with diagnostic potential and provides insight into biological mechanisms that will guide future research in KLS.

Qatar Diabetes Mobile Application Trial (QDMAT): an open-label randomised controlled trial to examine the impact of using a mobile application to improve diabetes care in type 2 diabetes mellitus-a study protocol.

BACKGROUND: Mobile health (mHealth) is increasingly advocated for diabetes management. It is unclear if mobile applications are effective in improving glycaemic control, clinical outcomes, quality of life and overall patient satisfaction in patients with type 2 diabetes (T2DM). A new mobile application was specifically built for people with T2DM with the help of the local expertise. The objective of the study was to evaluate the effectiveness of the mobile app. METHODS: The planned study is an ongoing open-label randomised controlled trial in which adults living with T2DM treated with insulin will be randomised 1:1 to the use of this diabetes application versus current standard care. The primary outcome will be the difference in mean HbA1c from baseline to 6 months. Other outcome measures include anthropometric measures, hypoglycaemic events, medication adjustments, number of clinical interactions and missed appointments and patient perceptions of their disease and diabetes self-management. The study will randomise 180 subjects for assessment of the primary outcome. DISCUSSION: We hypothesise that the diabetes-specific mobile application will improve glycaemic control, increase patient empowerment for self-management of diabetes and improve interaction between patients and healthcare providers. If the Qatar Diabetes Mobile Application Trial (QDMAT) demonstrates this, it will inform clinical services for the future self-management of T2DM. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03998267 . Registered on 26 June 2019.

The effectiveness of a structured group education programme for people with established type 2 diabetes in a multi-ethnic population in primary care: A cluster randomised trial.

BACKGROUND AND AIMS: Structured self-management education has been shown to be effective in type 2 diabetes (T2DM) but more research is needed to look at culturally appropriate programmes in ethnic minority groups, where prevalence of T2DM is higher and diagnosis earlier. The study tested the effectiveness of a group education programme for people with established T2DM in a multi-ethnic primary care population. METHODS AND RESULTS: Cluster randomised trial conducted in two multi-ethnic UK sites. Practices were randomised (1:1) to a structured T2DM group education programme or to continue with routine care. A culturally-adapted version was offered to South Asians, who formed the majority of ethnic minority participants. Other ethnic minority groups were invited to attend the standard programme. Primary outcome was change in HbA1c at 12 months. All analyses accounted for clustering and baseline value.367 participants (64(SD 10.8) years, 36% women, 34% from minority ethnic groups) were recruited from 31 clusters. At 12 months, there was no difference in mean change in HbA1c between the two groups (-0.10%; (95% CI: -0.37, 0.17). Subgroup analyses suggested the intervention was effective at lowering HbA1c in White European compared with ethnic minority groups. The intervention group lost more body weight than the control group (-0.82 kg at 6 months and -1.06 kg at 12 months; both p = 0.03). CONCLUSION: Overall, the programme did not result in HbA1c improvement but in subgroup analysis, a beneficial effect occurred in White Europeans. Findings emphasise a need to develop and evaluate culturally-relevant programmes for ethnic minority groups.

A Participatory Design Approach to Develop Visualization of Wearable Actigraphy Data for Health Care Professionals: Case Study in Qatar.

BACKGROUND: Several tools have been developed for health care professionals to monitor the physical activity of their patients, but most of these tools have been considering only the needs of users in North American and European countries and applicable for only specific analytic tasks. To our knowledge, no research study has utilized the participatory design (PD) approach in the Middle East region to develop such tools, involving all the stakeholders in the product development phases, and no clear use cases have been derived from such studies that could serve future development in the field. OBJECTIVE: This study aims to develop an interactive visualization tool (ActiVis) to support local health care professionals in monitoring the physical activity of their patients measured through wearable sensors, with the overall objective of improving the health of the Qatari population. METHODS: We used PD and user-centered design methodologies to develop ActiVis, including persona development, brainwriting, and heuristic walkthrough as part of user evaluation workshops; and use cases, heuristic walkthrough, interface walkthrough, and survey as part of expert evaluation sessions. RESULTS: We derived and validated 6 data analysis use cases targeted at specific health care professionals from a collaborative design workshop and an expert user study. These use cases led to improving the design of the ActiVis tool to support the monitoring of patients' physical activity by nurses and family doctors. The ActiVis research prototype (RP) compared favorably with the Fitbit Dashboard, showing the importance of design tools specific to end users' needs rather than relying on repurposing existing tools designed for other types of users. The use cases we derived happen to be culturally agnostic, despite our assumption that the local Muslim and Arabic culture could impact the design of such visualization tools. At last, taking a step back, we reflect on running collaborative design sessions in a multicultural environment and oil-based economy. CONCLUSIONS: Beyond the development of the ActiVis tool, this study can serve other visualization and human-computer interaction designers in the region to prepare their design projects and encourage health care professionals to engage with designers and engineers to improve the tools they use for supporting their daily routine. The development of the ActiVis tool for nurses, and other visualization tools specific to family doctors and clinician researchers, is still ongoing and we plan to integrate them into an operational platform for health care professionals in Qatar in the near future.

The Genetic Spectrum of Maturity-Onset Diabetes of the Young (MODY) in Qatar, a Population-Based Study.

Maturity-onset diabetes of the young (MODY) is a rare monogenic form of diabetes mellitus. In this study, we estimated the prevalence and genetic spectrum of MODY in the Middle Eastern population of Qatar using whole-genome sequencing (WGS) of 14,364 subjects from the population-based Qatar biobank (QBB) cohort. We focused our investigations on 14 previously identified genes ascribed to the cause of MODY and two potentially novel MODY-causing genes, RFX6 and NKX6-1. Genetic variations within the 16 MODY-related genes were assessed for their pathogenicity to identify disease-causing mutations. Analysis of QBB phenotype data revealed 72 subjects (0.5%) with type 1 diabetes, 2915 subjects (20.3%) with type 2 diabetes and 11,377 (79.2%) without diabetes. We identified 22 mutations in 67 subjects that were previously reported in the Human Genetic Mutation Database (HGMD) as disease-causing (DM) or likely disease causing (DM?) for MODY. We also identified 28 potentially novel MODY-causing mutations, predicted to be among the top 1% most deleterious mutations in the human genome, which showed complete (100%) disease penetrance in 34 subjects. Overall, we estimated that MODY accounts for around 2.2-3.4% of diabetes patients in Qatar. This is the first population-based study to determine the genetic spectrum and estimate the prevalence of MODY in the Middle East. Further research to characterize the newly identified mutations is warranted.