Communications Medicine

BackgroundInter-individual variability in lipid response to statin therapy poses a major challenge in cardiovascular risk reduction, particularly among patients with type 2 diabetes mellitus (T2DM), who exhibit complex dyslipidemia and elevated cardiovascular risk. While randomized trials provide population-level estimates of treatment efficacy, individualized prediction of lipid changes, and corresponding uncertainty, remains limited. Using rich clinical data from the Action to Control Cardiova...

The increasing availability of electronic health records (EHRs) provides opportunities to apply machine learning (ML) methods in support of clinical decision-making. The temporal nature of laboratory values in EHR data records makes them particularly suitable for temporal deep learning (DL) architectures that model patient trajectories over time. However, despite this potential, the application of temporal DL models to longitudinal laboratory data has largely been limited to intensive care unit ...

BackgroundCurrent glycemic monitoring exhibits a critical temporal gap between fructosamine (14-21 days) and HbA1c (90-120 days), limiting timely therapeutic adjustments. The Glycated Protein Precipitation Index (GPPI) represents a novel biomarker potentially bridging this interval. ObjectiveTo validate GPPIs correlation with established glycemic markers and determine its clinical utility for intermediate-term glycemic assessment. Methods: This prospective validation study will enroll 200 diabe...

Aims/HypothesisUnderstanding heterogeneous patient responses to various glucose-lowering therapies is crucial for advancing personalized treatment approaches and optimizing outcomes for type 2 diabetes mellitus. While average treatment effects are known for many drug classes, patient responses may differ by underlying clinical and demographic factors. We hypothesize that major glucose-lowering drug classes exhibit heterogeneous treatment effects (HTE) across patient subgroups defined by key clin...

Bias in machine learning is a persistent challenge because it can create unfair outcomes, limit generalization, and reduce trust in real-world applications. A key source of this problem is shortcut learning, where models exploit signals linked to sensitive attributes, such as data source or collection site, instead of relying on task, relevant features. To tackle this, we propose the Deceptive Signal metric, a novel quantitative measure designed to assess the extent of a models reliance on hidde...

BACKGROUNDCardiovascular disease (CVD) is a leading cause of diabetes-related mortality in Mexico. Although diabetes subgroups capture underlying disease heterogeneity, their association and utility for risk prediction for fatal CVD in Mexican adults remain unclear. METHODSWe analyzed 24,943 adults with diabetes from the Mexico City Prospective Study. Participants were classified into mild obesity-related (MOD), severe insulin-deficient (SIDD), severe insulin-resistant (SIRD), and mild age-rela...

BackgroundGLP-1 receptor agonists (GLP-1RAs) and SGLT2 inhibitors (SGLT2Is) have established cardiovascular benefits for patients with type 2 diabetes mellitus (T2DM), with similar class-level effectiveness found in previous studies. However, real-world comparative effectiveness assessments of individual agents remain limited. ObjectivesTo compare the cardiovascular effectiveness of individual GLP-1RAs and SGLT2Is. MethodsWe conducted a multi-national, retrospective, new-user active-comparator...

The daily cortisol cycle is a critical indicator of hypothalamic-pituitary-adrenal (HPA) axis function. The current analytical approaches produce several outputs difficult to integrate into simple statistical models, clinical workflows, and ML/AI pipelines requiring single-value inputs. We developed the Cortisol Sine Score (CSS), a model-free scalar metric that quantifies daily cortisol exposure by computing a weighted sum of cortisol measurements across the day, using sine-transformed time-of-d...

Metabolic dysfunction-associated steatotic liver disease (MASLD) arises from excessive hepatic fat accumulation that triggers inflammation and liver injury. It is the most prevalent chronic liver disease worldwide, affecting more than one quarter of adults. Despite this, MASLD is often underdiagnosed, making it more difficult to perform genome-wide association studies (GWAS). In this paper, we implemented a machine learning (ML)-guided GWAS framework to identify genetic risk factors for MASLD ac...

BackgroundKidney disease refers to a broad range of disorders that impair renal structure and function. Among these, chronic kidney disease (CKD) is the most prevalent worldwide, affecting approximately 10% of the global adult population. While large-scale omics studies have identified numerous molecular associations with kidney function and disease, these insights often remain isolated within individual data layers, hindering a systems-level understanding of the functional interplay between gen...

BackgroundKlebsiella pneumoniae is a common cause of neonatal sepsis in Africa, and is frequently hospital acquired. We recently reported an outbreak of multidrug-resistant K. pneumoniae sepsis amongst neonates at a rural hospital in The Gambia, West Africa, involving 57 cases and case fatality of 60%. Here we undertook a retrospective pathogen genomic epidemiology study of clinical and environmental K. pneumoniae isolated during the outbreak, to identify the outbreak strain, refine the epidemic...

BackgroundKlebsiella pneumoniae complex (Kp) is a relevant neonatal pathogen colonizing preterm infants. While outbreak investigations often focus on multidrug-resistant strains, the epidemiology and genomic dynamics of wild-type Kp in nonoutbreak neonatal intensive care unit (NICU) settings remain elusive. MethodsWe conducted a 30-month (October 2021 to March 2024) cohort study with weekly active, unselective colonization surveillance of all NICU patients to identify risk factors for nosocomia...

BackgroundEffective interventions are needed to support co-creation of diabetes care plans that fit patients lives. We evaluated the QBSafe agenda-setting kit (14 conversation cards) for its impact on care fit and glycemic control when added to usual primary care. MethodsThis single-center, clinician-level cluster-randomized, open-label trial was conducted at a federally qualified health center in New Haven, Connecticut (ClinicalTrials.gov NCT05553912). Clinicians and their patients with type 2...

1PurposeThe KIND (KINder mit Diabetes) cohort investigates diabetic peripheral neuropathy (DPN) in paediatric type 1 diabetes (T1D). Current guidelines recommend DPN screening at puberty or from 11 years and 2-5 years after T1D diagnosis, yet subclinical neurophysiological changes occur within the first 2 years. The cohort examines: (1) longitudinal associations between glycaemic metrics (HbA1c and continuous glucose monitoring-derived variability metrics) and peripheral nerve function and struc...

ObjectiveHbA1c thresholds used to define dysglycemia in autoantibody-positive individuals at risk for type 1 diabetes do not account for age-related increases in HbA1c and may overestimate progression risk in adults. We evaluated whether age-adjusted HbA1c or a higher HbA1c threshold improves risk stratification across age groups. Research Design and MethodsWe analyzed 5,024 autoantibody-positive relatives (3,720 children and 1,304 adults) participating in the TrialNet Pathway to Prevention stu...

Identifying individuals at risk of early onset type 1 diabetes (diagnosed <2 years) would be highly beneficial in reducing risk of severe diabetic ketoacidosis (DKA) for those with extreme autoimmunity. We aimed to investigate whether genetic variation contributes to heterogeneity in age of type 1 diabetes onset, focusing on those diagnosed <2 years and ages previously defined by histological differences. We carried out association testing on 6773 individuals with type 1 diabetes and tested for ...

AimsThe gut microbiome has been implicated in type 2 diabetes progression, but reproducible biomarkers across studies remain limited due to technical and population heterogeneity. This study investigated whether specific gut microbiome shifts occur progressively across stages of type 2 diabetes. MethodsWe systematically reanalysed 16S rRNA datasets from 12 published studies (n=1,247 samples) after quality control, examining five groups (healthy controls, prediabetes (PD), new-onset type 2 diabe...

The detection of monogenic diabetes illustrates the potential of precision medicine, with treatments tailored to specific genes and diagnosis involving targeted genetic testing. Current detection criteria are derived from White populations. We investigated detection of monogenic diabetes in an unselected multiethnic cohort comprising 1,706 participants diagnosed with diabetes before the age of 30-years. Using broad biomarker criteria (triple pancreatic antibody negative and detectable C-peptide...

BackgroundThe Kidney Precision Medicine Project (KPMP) consortium aims to redefine chronic kidney disease (CKD) by integrating clinical, pathological, and molecular tissue data from kidney biopsies. Here, we demonstrate how biopsy data in CKD can clarify disease etiology and contribute to understandings of disease pathophysiology and clinical prognosis. MethodsThe KPMP is obtaining research kidney biopsies from individuals with CKD (defined as an estimated glomerular filtration rate [eGFR] < 60...

Acute kidney injury (AKI) and chronic kidney disease (CKD) are two interconnected clinical conditions, both defined by degree of functional impairment, but with heterogeneous clinical trajectories. Using new transcriptomic technologies, recent studies have described the cellular diversity in the healthy and injured kidney at the single cell level. Here, we used single nucleus transcriptomics to investigate the molecular diversity and commonalities in kidney biopsies from over 150 participants wi...