Phage-assisted evolution and protein engineering yield compact, efficient prime editors.

Prime editing enables a wide variety of precise genome edits in living cells. Here we use protein evolution and engineering to generate prime editors with reduced size and improved efficiency. Using phage-assisted evolution, we improved editing efficiencies of compact reverse transcriptases by up to 22-fold and generated prime editors that are 516-810 base pairs smaller than the current-generation editor PEmax. We discovered that different reverse transcriptases specialize in different types of edits and used this insight to generate reverse transcriptases that outperform PEmax and PEmaxΔRNaseH, the truncated editor used in dual-AAV delivery systems. Finally, we generated Cas9 domains that improve prime editing. These resulting editors (PE6a-g) enhance therapeutically relevant editing in patient-derived fibroblasts and primary human T-cells. PE6 variants also enable longer insertions to be installed in vivo following dual-AAV delivery, achieving 40% loxP insertion in the cortex of the murine brain, a 24-fold improvement compared to previous state-of-the-art prime editors.

Scalable, Continuous Evolution of Genes at Mutation Rates above Genomic Error Thresholds.

Directed evolution is a powerful approach for engineering biomolecules and understanding adaptation. However, experimental strategies for directed evolution are notoriously labor intensive and low throughput, limiting access to demanding functions, multiple functions in parallel, and the study of molecular evolution in replicate. We report OrthoRep, an orthogonal DNA polymerase-plasmid pair in yeast that stably mutates ∼100,000-fold faster than the host genome in vivo, exceeding the error threshold of genomic replication that causes single-generation extinction. User-defined genes in OrthoRep continuously and rapidly evolve through serial passaging, a highly straightforward and scalable process. Using OrthoRep, we evolved drug-resistant malarial dihydrofolate reductases (DHFRs) in 90 independent replicates. We uncovered a more complex fitness landscape than previously realized, including common adaptive trajectories constrained by epistasis, rare outcomes that avoid a frequent early adaptive mutation, and a suboptimal fitness peak that occasionally traps evolving populations. OrthoRep enables a new paradigm of routine, high-throughput evolution of biomolecular and cellular function.

A Map-like Micro-Organization of Grid Cells in the Medial Entorhinal Cortex.

How the topography of neural circuits relates to their function remains unclear. Although topographic maps exist for sensory and motor variables, they are rarely observed for cognitive variables. Using calcium imaging during virtual navigation, we investigated the relationship between the anatomical organization and functional properties of grid cells, which represent a cognitive code for location during navigation. We found a substantial degree of grid cell micro-organization in mouse medial entorhinal cortex: grid cells and modules all clustered anatomically. Within a module, the layout of grid cells was a noisy two-dimensional lattice in which the anatomical distribution of grid cells largely matched their spatial tuning phases. This micro-arrangement of phases demonstrates the existence of a topographical map encoding a cognitive variable in rodents. It contributes to a foundation for evaluating circuit models of the grid cell network and is consistent with continuous attractor models as the mechanism of grid formation.

Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting.

Analysis of single cell transcriptomics (scRNA-seq) data is typically performed after subsetting to highly variable genes (HVGs). Here, we show that Entropy Sorting provides an alternative mathematical framework for feature selection. On synthetic datasets, continuous Entropy Sort Feature Weighting (cESFW) outperforms HVG selection in distinguishing cell-state-specific genes. We apply cESFW to six merged scRNA-seq datasets spanning human early embryo development. Without smoothing or augmenting the raw counts matrices, cESFW generates a high-resolution embedding displaying coherent developmental progression from eight-cell to post-implantation stages and delineating 15 distinct cell states. The embedding highlights sequential lineage decisions during blastocyst development, while unsupervised clustering identifies branch point populations obscured in previous analyses. The first branching region, where morula cells become specified for inner cell mass or trophectoderm, includes cells previously asserted to lack a developmental trajectory. We quantify the relatedness of different pluripotent stem cell cultures to distinct embryo cell types and identify marker genes of naïve and primed pluripotency. Finally, by revealing genes with dynamic lineage-specific expression, we provide markers for staging progression from morula to blastocyst.

On the surprising effectiveness of a simple matrix exponential derivative approximation, with application to global SARS-CoV-2.

The continuous-time Markov chain (CTMC) is the mathematical workhorse of evolutionary biology. Learning CTMC model parameters using modern, gradient-based methods requires the derivative of the matrix exponential evaluated at the CTMC's infinitesimal generator (rate) matrix. Motivated by the derivative's extreme computational complexity as a function of state space cardinality, recent work demonstrates the surprising effectiveness of a naive, first-order approximation for a host of problems in computational biology. In response to this empirical success, we obtain rigorous deterministic and probabilistic bounds for the error accrued by the naive approximation and establish a "blessing of dimensionality" result that is universal for a large class of rate matrices with random entries. Finally, we apply the first-order approximation within surrogate-trajectory Hamiltonian Monte Carlo for the analysis of the early spread of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across 44 geographic regions that comprise a state space of unprecedented dimensionality for unstructured (flexible) CTMC models within evolutionary biology.

Dark continuous noise from mutant G90D-rhodopsin predominantly underlies congenital stationary night blindness.

Congenital stationary night blindness (CSNB) is an inherited retinal disease that causes a profound loss of rod sensitivity without severe retinal degeneration. One well-studied rhodopsin point mutant, G90D-Rho, is thought to cause CSNB because of its constitutive activity in darkness causing rod desensitization. However, the nature of this constitutive activity and its precise molecular source have not been resolved for almost 30 y. In this study, we made a knock-in (KI) mouse line with a very low expression of G90D-Rho (equal in amount to ~0.1% of normal rhodopsin, WT-Rho, in WT rods), with the remaining WT-Rho replaced by REY-Rho, a mutant with a very low efficiency of activating transducin due to a charge reversal of the highly conserved ERY motif to REY. We observed two kinds of constitutive noise: one being spontaneous isomerization (R*) of G90D-Rho at a molecular rate (R* s-1) 175-fold higher than WT-Rho and the other being G90D-Rho-generated dark continuous noise comprising low-amplitude unitary events occurring at a very high molecular rate equivalent in effect to ~40,000-fold of R* s-1 from WT-Rho. Neither noise type originated from G90D-Opsin because exogenous 11-cis-retinal had no effect. Extrapolating the above observations at low (0.1%) expression of G90D-Rho to normal disease exhibited by a KI mouse model with RhoG90D/WTand RhoG90D/G90D genotypes predicts the disease condition very well quantitatively. Overall, the continuous noise from G90D-Rho therefore predominates, constituting the major equivalent background light causing rod desensitization in CSNB.

Methods for the directed evolution of biomolecular interactions.

Noncovalent interactions between biomolecules such as proteins and nucleic acids coordinate all cellular processes through changes in proximity. Tools that perturb these interactions are and will continue to be highly valuable for basic and translational scientific endeavors. By taking cues from natural systems, such as the adaptive immune system, we can design directed evolution platforms that can generate proteins that bind to biomolecules of interest. In recent years, the platforms used to direct the evolution of biomolecular binders have greatly expanded the range of types of interactions one can evolve. Herein, we review recent advances in methods to evolve protein-protein, protein-RNA, and protein-DNA interactions.

Spatom: a graph neural network for structure-based protein-protein interaction site prediction.

Accurate identification of protein-protein interaction (PPI) sites remains a computational challenge. We propose Spatom, a novel framework for PPI site prediction. This framework first defines a weighted digraph for a protein structure to precisely characterize the spatial contacts of residues, then performs a weighted digraph convolution to aggregate both spatial local and global information and finally adds an improved graph attention layer to drive the predicted sites to form more continuous region(s). Spatom was tested on a diverse set of challenging protein-protein complexes and demonstrated the best performance among all the compared methods. Furthermore, when tested on multiple popular proteins in a case study, Spatom clearly identifies the interaction interfaces and captures the majority of hotspots. Spatom is expected to contribute to the understanding of protein interactions and drug designs targeting protein binding.

Direct Electron Transfer-Type Oxidoreductases for Biomedical Applications.

Among the various types of enzyme-based biosensors, sensors utilizing enzymes capable of direct electron transfer (DET) are recognized as the most ideal. However, only a limited number of redox enzymes are capable of DET with electrodes, that is, dehydrogenases harboring a subunit or domain that functions specifically to accept electrons from the redox cofactor of the catalytic site and transfer the electrons to the external electron acceptor. Such subunits or domains act as built-in mediators for electron transfer between enzymes and electrodes; consequently, such enzymes enable direct electron transfer to electrodes and are designated as DET-type enzymes. DET-type enzymes fall into several categories, including redox cofactors of catalytic reactions, built-in mediators for DET with electrodes and by their protein hierarchic structures, DET-type oxidoreductases with oligomeric structures harboring electron transfer subunits, and monomeric DET-type oxidoreductases harboring electron transfer domains. In this review, we cover the science of DET-type oxidoreductases and their biomedical applications. First, we introduce the structural biology and current understanding of DET-type enzyme reactions. Next, we describe recent technological developments based on DET-type enzymes for biomedical applications, such as biosensors and biochemical energy harvesting for self-powered medical devices. Finally, after discussing how to further engineer and create DET-type enzymes, we address the future prospects for DET-type enzymes in biomedical engineering.

Switching off: disruptive TMS reveals distinct contributions of the posterior middle temporal gyrus and angular gyrus to bilingual speech production.

The role of the left temporoparietal cortex in speech production has been extensively studied during native language processing, proving crucial in controlled lexico-semantic retrieval under varying cognitive demands. Yet, its role in bilinguals, fluent in both native and second languages, remains poorly understood. Here, we employed continuous theta burst stimulation to disrupt neural activity in the left posterior middle-temporal gyrus (pMTG) and angular gyrus (AG) while Italian-Friulian bilinguals performed a cued picture-naming task. The task involved between-language (naming objects in Italian or Friulian) and within-language blocks (naming objects ["knife"] or associated actions ["cut"] in a single language) in which participants could either maintain (non-switch) or change (switch) instructions based on cues. During within-language blocks, cTBS over the pMTG entailed faster naming for high-demanding switch trials, while cTBS to the AG elicited slower latencies in low-demanding non-switch trials. No cTBS effects were observed in the between-language block. Our findings suggest a causal involvement of the left pMTG and AG in lexico-semantic processing across languages, with distinct contributions to controlled vs. "automatic" retrieval, respectively. However, they do not support the existence of shared control mechanisms within and between language(s) production. Altogether, these results inform neurobiological models of semantic control in bilinguals.

Higher-order effects, continuous species interactions, and trait evolution shape microbial spatial dynamics.

The assembly and maintenance of microbial diversity in natural communities, despite the abundance of toxin-based antagonistic interactions, presents major challenges for biological understanding. A common framework for investigating such antagonistic interactions involves cyclic dominance games with pairwise interactions. The incorporation of higher-order interactions in such models permits increased levels of microbial diversity, especially in communities in which antibiotic-producing, sensitive, and resistant strains coexist. However, most such models involve a small number of discrete species, assume a notion of pure cyclic dominance, and focus on low mutation rate regimes, none of which well represent the highly interlinked, quickly evolving, and continuous nature of microbial phenotypic space. Here, we present an alternative vision of spatial dynamics for microbial communities based on antagonistic interactions-one in which a large number of species interact in continuous phenotypic space, are capable of rapid mutation, and engage in both direct and higher-order interactions mediated by production of and resistance to antibiotics. Focusing on toxin production, vulnerability, and inhibition among species, we observe highly divergent patterns of diversity and spatial community dynamics. We find that species interaction constraints (rather than mobility) best predict spatiotemporal disturbance regimes, whereas community formation time, mobility, and mutation size best explain patterns of diversity. We also report an intriguing relationship among community formation time, spatial disturbance regimes, and diversity dynamics. This relationship, which suggests that both higher-order interactions and rapid evolution are critical for the origin and maintenance of microbial diversity, has broad-ranging links to the maintenance of diversity in other systems.

Heritable changes in division speed accompany the diversification of single T cell fate.

Rapid clonal expansion of antigen-specific T cells is a fundamental feature of adaptive immune responses. It enables the outgrowth of an individual T cell into thousands of clonal descendants that diversify into short-lived effectors and long-lived memory cells. Clonal expansion is thought to be programmed upon priming of a single naive T cell and then executed by homogenously fast divisions of all of its descendants. However, the actual speed of cell divisions in such an emerging "T cell family" has never been measured with single-cell resolution. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naive CD8+ T cell throughout up to ten divisions of activation-induced proliferation. This comprehensive mapping of T cell family trees identifies a short burst phase, in which division speed is homogenously fast and maintained independent of external cytokine availability or continued T cell receptor stimulation. Thereafter, however, division speed diversifies, and model-based computational analysis using a Bayesian inference framework for tree-structured data reveals a segregation into heritably fast- and slow-dividing branches. This diversification of division speed is preceded already during the burst phase by variable expression of the interleukin-2 receptor alpha chain. Later it is accompanied by selective expression of memory marker CD62L in slower dividing branches. Taken together, these data demonstrate that T cell clonal expansion is structured into subsequent burst and diversification phases, the latter of which coincides with specification of memory versus effector fate.

Continuous Strain Regulation of Palladium-Gold at the Atomic Level.

Revealing the effect of surface structure changes on the electrocatalytic performance is beneficial to the development of highly efficient catalysts. However, precise regulation of the catalyst surface at the atomic level remains challenging. Here, we present a continuous strain regulation of palladium (Pd) on gold (Au) via a mechanically controllable surface strain (MCSS) setup. It is found that the structural changes induced by the strain setup can accelerate electron transfer at the solid-liquid interface, thus achieving a significantly improved performance toward hydrogen evolution reaction (HER). In situ X-ray diffraction (XRD) experiments further confirm that the enhanced activity is attributed to the increased interplanar spacing resulting from the applied strain. Theoretical calculations reveal that the tensile strain modulates the electronic structure of the Pd active sites and facilitates the desorption of the hydrogen intermediates. This work provides an effective approach for revealing the relationships between the electrocatalyst surface structure and catalytic activity.

Physiological Microenvironment Dependent Self-Cross-Linking of Multifunctional Nanohybrid for Prolonged Antibacterial Therapy via Synergistic Chemodynamic-Photothermal-Biological Processes.

Herein, a multifunctional nanohybrid (PL@HPFTM nanoparticles) was fabricated to perform the integration of chemodynamic therapy, photothermal therapy, and biological therapy over the long term at a designed location for continuous antibacterial applications. The PL@HPFTM nanoparticles consisted of a polydopamine/hemoglobin/Fe2+ nanocomplex with comodification of tetrazole/alkene groups on the surface as well as coloading of antimicrobial peptides and luminol in the core. During therapy, the PL@HPFTM nanoparticles would selectively cross-link to surrounding bacteria via tetrazole/alkene cycloaddition under chemiluminescence produced by the reaction between luminol and overexpressed H2O2 at the infected area. The resulting PL@HPFTM network not only significantly damaged bacteria by Fe2+-catalyzed ROS production, effective photothermal conversion, and sustained release of antimicrobial peptides but dramatically enhanced the retention time of these therapeutic agents for prolonged antibacterial therapy. Both in vitro and in vivo results have shown that our PL@HPFTM nanoparticles have much higher bactericidal efficiency and remarkably longer periods of validity than free antibacterial nanoparticles.

Incorporating functional annotation with bilevel continuous shrinkage for polygenic risk prediction.

BACKGROUND: Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. RESULTS: We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. CONCLUSIONS: By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

Threshold of hyperglycaemia associated with mortality in critically ill patients: a multicentre, prospective, observational study using continuous glucose monitoring.

AIMS/HYPOTHESIS: Continuous glucose monitoring (CGM) provides comprehensive information on the exposure to dysglycaemia. This study aimed to investigate the threshold of hyperglycaemia related to mortality risk in critically ill patients using CGM technology. METHODS: A total of 293 adult critically ill patients admitted to intensive care units of five medical centres were prospectively included between May 2020 and November 2021. Participants wore intermittently scanned CGM for a median of 12.0 days. The relationships between different predefined time above ranges (TARs), with the thresholds of hyperglycaemia ranging from 7.8 to 13.9 mmol/l (140-250 mg/dl), and in-hospital mortality risk were assessed by multivariate Cox proportional regression analysis. Time in ranges (TIRs) of 3.9 mmol/l (70 mg/dl) to the predefined hyperglycaemic thresholds were also assessed. RESULTS: Overall, 66 (22.5%) in-hospital deaths were identified. Only TARs with a threshold of 10.5 mmol/l (190 mg/dl) or above were significantly associated with the risk of in-hospital mortality, after adjustment for covariates. Furthermore, as the thresholds for TAR increased from 10.5 mmol/l to 13.9 mmol/l (190 mg/dl to 250 mg/dl), the hazards of in-hospital mortality increased incrementally with every 10% increase in TARs. Similar results were observed concerning the associations between TIRs with various upper thresholds and in-hospital mortality risk. For per absolute 10% decrease in TIR 3.9-10.5 mmol/l (70-190 mg/dl), the risk of in-hospital mortality was increased by 12.1% (HR 1.121 [95% CI 1.003, 1.253]). CONCLUSIONS/INTERPRETATION: A glucose level exceeding 10.5 mmol/l (190 mg/dl) was significantly associated with higher risk of in-hospital mortality in critically ill patients.

Accuracy of continuous glucose monitoring in the hospital setting: an observational study.

AIMS/HYPOTHESIS: Continuous glucose monitoring (CGM) improves glycaemic outcomes in the outpatient setting; however, there are limited data regarding CGM accuracy in hospital. METHODS: We conducted a prospective, observational study comparing CGM data from blinded Dexcom G6 Pro sensors with reference point of care and laboratory glucose measurements during participants' hospitalisations. Key accuracy metrics included the proportion of CGM values within ±20% of reference glucose values >5.6 mmol/l or within ±1.1 mmol/l of reference glucose values ≤5.6 mmol/l (%20/20), the mean and median absolute relative difference between CGM and reference value (MARD and median ARD, respectively) and Clarke error grid analysis (CEGA). A retrospective calibration scheme was used to determine whether calibration improved sensor accuracy. Multivariable regression models and subgroup analyses were used to determine the impact of clinical characteristics on accuracy assessments. RESULTS: A total of 326 adults hospitalised on 19 medical or surgical non-intensive care hospital floors were enrolled, providing 6648 matched glucose pairs. The %20/20 was 59.5%, the MARD was 19.2% and the median ARD was 16.8%. CEGA showed that 98.2% of values were in zone A (clinically accurate) and zone B (benign). Subgroups with lower accuracy metrics included those with severe anaemia, renal dysfunction and oedema. Application of a once-daily morning calibration schedule improved accuracy (MARD 11.4%). CONCLUSIONS/INTERPRETATION: The CGM accuracy when used in hospital may be lower than that reported in the outpatient setting, but this may be improved with appropriate patient selection and daily calibration. Further research is needed to understand the role of CGM in inpatient settings.

Cost-utility analysis of Dexcom G6 real-time continuous glucose monitoring versus FreeStyle Libre 1 intermittently scanned continuous glucose monitoring in adults with type 1 diabetes in Belgium.

AIMS/HYPOTHESIS: The aim of this study was to assess the long-term cost-effectiveness of Dexcom G6 real-time continuous glucose monitoring (rtCGM) with alert functionality compared with FreeStyle Libre 1 intermittently scanned continuous glucose monitoring (isCGM) without alerts in adults with type 1 diabetes in Belgium. METHODS: The IQVIA CORE Diabetes Model was used to estimate cost-effectiveness. Input data for the simulated baseline cohort were sourced from the randomised ALERTT1 trial (ClinicalTrials.gov. REGISTRATION NO: NCT03772600). The age of the participants was 42.9 ± 14.1 years (mean ± SD), and the baseline HbA1c was 57.8 ± 9.5 mmol/mol (7.4 ± 0.9%). Participants using rtCGM showed a reduction in HbA1c of 3.6 mmol/mol (0.36 percentage points) based on the 6-month mean between-group difference. In the base case, both rtCGM and isCGM were priced at €3.92/day (excluding value-added tax [VAT]) according to the Belgian reimbursement system. The analysis was performed from a Belgian healthcare payer perspective over a lifetime time horizon. Health outcomes were expressed as quality-adjusted life years. Probabilistic and one-way sensitivity analyses were used to account for parameter uncertainty. RESULTS: In the base case, rtCGM dominated isCGM, resulting in lower diabetes-related complication costs and better health outcomes. The associated main drivers favouring rtCGM were lower HbA1c, fewer severe hypoglycaemic events and reduced fear of hypoglycaemia. The results were robust under a wide range of one-way sensitivity analyses. In models where the price of rtCGM is €5.11/day (a price increase of 30.4%) or €12.34/day (a price increase of 214.8%), rtCGM was cost-neutral or reached an incremental cost-effectiveness ratio of €40,000 per quality-adjusted life year, respectively. CONCLUSIONS/INTERPRETATION: When priced similarly, Dexcom G6 rtCGM with alert functionality has both economic and clinical benefits compared with FreeStyle Libre 1 isCGM without alerts in adults with type 1 diabetes in Belgium, and appears to be a cost-effective glucose monitoring modality. Trial registration ClinicalTrials.gov NCT03772600.

The use of technology in type 2 diabetes and prediabetes: a narrative review.

The increasing incidence of type 2 diabetes, which represents 90% of diabetes cases globally, is a major public health concern. Improved glucose management reduces the risk of vascular complications and mortality; however, only a small proportion of the type 2 diabetes population have blood glucose levels within the recommended treatment targets. In recent years, diabetes technologies have revolutionised the care of people with type 1 diabetes, and it is becoming increasingly evident that people with type 2 diabetes can also benefit from these advances. In this review, we describe the current knowledge regarding the role of technologies for people living with type 2 diabetes and the evidence supporting their use in clinical practice. We conclude that continuous glucose monitoring systems deliver glycaemic benefits for individuals with type 2 diabetes, whether treated with insulin or non-insulin therapy; further data are required to evaluate the role of these systems in those with prediabetes (defined as impaired glucose tolerance and/or impaired fasting glucose and/or HbA1c levels between 39 mmol/mol [5.7%] and 47 mmol/mol [6.4%]). The use of insulin pumps seems to be safe and effective in people with type 2 diabetes, especially in those with an HbA1c significantly above target. Initial results from studies exploring the impact of closed-loop systems in type 2 diabetes are promising. We discuss directions for future research to fully understand the potential benefits of integrating evidence-based technology into care for people living with type 2 diabetes and prediabetes.

Glycaemia risk index uncovers distinct glycaemic variability patterns associated with remission status in type 1 diabetes.

AIMS/HYPOTHESIS: The aim of this work was to define a unique remission status using glycaemia risk index (GRI) and other continuous glucose monitoring (CGM) metrics in individuals with type 1 diabetes for improved phenotyping. METHODS: A group of 140 individuals with type 1 diabetes were recruited for a cross-sectional study. The participants were categorised into four groups based on their remission status, which was defined as insulin-dose-adjusted A1c (IDAA1c) <9 or C-peptide ≥300 pmol/l: new-onset (n=24); mid-remission (n=44); post-remission (n=44); and non-remission (individuals who did not experience remission, n=28). Participants in the remission phase were referred to as 'remitters', while those who were not in the remission phase were referred to as 'non-remitters', the latter group including new-onset, post-remission and non-remission participants. Clinical variables such as HbA1c, C-peptide and insulin daily dose, as well as IDAA1C and CGM data, were collected. The patterns of CGM metrics were analysed for each group using generalised estimating equations to investigate the glycaemic variability patterns associated with remission status. Then, unsupervised hierarchical clustering was used to place the participants into subgroups based on GRI and other CGM core metrics. RESULTS: The glycaemic variability patterns associated with remission status were found to be distinct based on the circadian CGM metrics. Remitters showed improved control of blood glucose levels over 14 days within the range of 3.9-10 mmol/l, and lower GRI compared with non-remitters (p<0.001). Moreover, GRI strongly correlated with IDAA1C (r=0.62; p<0.001) and was sufficient to distinguish remitters from non-remitters. Further, four subgroups demonstrating distinct patterns of glycaemic variability associated with different remission status were identified by clustering on CGM metrics: remitters with low risk of dysglycaemia; non-remitters with high risk of hypoglycaemia; non-remitters with high risk of hyperglycaemia; and non-remitters with moderate risk of dysglycaemia. CONCLUSIONS/INTERPRETATION: GRI, an integrative index, together with other traditional CGM metrics, helps to identify different glycaemic variability patterns; this might provide specifically tailored monitoring and management strategies for individuals in the various subclusters.