Daily Endocrinology Research Analysis
Three advances span precision therapeutics, mechanistic cardiometabolic biology, and population health. A Lancet model uses routine clinical data to personalize selection among five glucose-lowering drug classes, improving 12‑month HbA1c when therapy matches predictions. A mechanistic study reveals that BDH1 rewires ketone/epigenetic signaling to suppress LCN2 and mitigate diabetic cardiomyopathy, while a multicountry cohort quantifies high type 2 diabetes incidence and modifiable risks in sub-S
Summary
Three advances span precision therapeutics, mechanistic cardiometabolic biology, and population health. A Lancet model uses routine clinical data to personalize selection among five glucose-lowering drug classes, improving 12‑month HbA1c when therapy matches predictions. A mechanistic study reveals that BDH1 rewires ketone/epigenetic signaling to suppress LCN2 and mitigate diabetic cardiomyopathy, while a multicountry cohort quantifies high type 2 diabetes incidence and modifiable risks in sub-Saharan Africa.
Research Themes
- Precision prescribing and treatment heterogeneity in type 2 diabetes
- Epigenetic–metabolic mechanisms in diabetic cardiomyopathy
- Global epidemiology and risk stratification of type 2 diabetes in sub-Saharan Africa
Selected Articles
1. A five-drug class model using routinely available clinical features to optimise prescribing in type 2 diabetes: a prediction model development and validation study.
Using UK CPRD data, the authors developed and validated a model that predicts the most effective of five glucose-lowering drug classes for individual patients based on routine clinical features. Only 15.2% of initiations aligned with model-optimal therapy; patients receiving model-concordant therapy achieved lower 12‑month HbA1c than those on discordant therapy.
Impact: Delivers a scalable precision-prescribing tool with immediate translational relevance and potential to change T2D treatment selection. Publication in Lancet underscores methodological and clinical significance.
Clinical Implications: Embedding the model into EHR decision support could guide initial or add-on therapy selection (e.g., between SGLT2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, TZDs, sulfonylureas) to maximize HbA1c reduction, potentially reducing therapeutic inertia and unnecessary switching.
Key Findings
- A five-drug class prediction model was developed and validated using 100,107 drug initiations in CPRD.
- Across 212,166 initiations, only 15.2% matched the model-predicted optimal therapy.
- Model-concordant treatment was associated with lower observed 12-month HbA1c than discordant treatment.
Methodological Strengths
- Large, real-world dataset with model development and validation cohorts
- Standardized outcome (12-month HbA1c) enabling objective comparison across drug classes
Limitations
- Observational design may entail residual confounding and indication bias
- Generalizability beyond UK settings and non-glycemic outcomes (e.g., CV-renal endpoints) require further study
Future Directions: Prospective pragmatic trials testing clinical outcomes, health-economic evaluations, and deployment as CDS tools with continuous recalibration across diverse populations.
BACKGROUND: Data to support individualised choice of optimal glucose-lowering therapy are scarce for people with type 2 diabetes. We aimed to establish whether routinely available clinical features can be used to predict the relative glycaemic effectiveness of five glucose-lowering drug classes. METHODS: We developed and validated a five-drug class model to predict the relative glycaemic effectiveness, in terms of absolute 12-month glycated haemoglobin (HbA FINDINGS: The five-drug class model was developed from 100 107 drug initiations in CPRD. In the overall CPRD cohort (combined development and validation cohorts), 32 305 (15·2%) of 212 166 drug initiations were of the model-predicted optimal therapy. In model-concordant groups, mean observed 12-month HbA INTERPRETATION: We have developed a five-drug class model that uses routine clinical data to identify optimal glucose-lowering therapies for people with type 2 diabetes. Individuals on model-predicted optimal therapy had lower 12-month HbA FUNDING: UK Medical Research Council.
2. BDH1 overexpression alleviates diabetic cardiomyopathy through inhibiting H3K9bhb-mediated transcriptional activation of LCN2.
BDH1, diminished in diabetic hearts, protects against diabetic cardiomyopathy by reprogramming ketone metabolism to reduce H3K9 β-hydroxybutyrylation at the LCN2 promoter, thereby suppressing LCN2 and NF-κB activity. Genetic gain-of-function preserved diastolic function and reduced apoptosis, fibrosis, and inflammation; pharmacologic inhibition of β-hydroxybutyrylation recapitulated protection.
Impact: Reveals a first-in-kind epigenetic-ketone mechanism linking BDH1 to LCN2 and NF-κB in diabetic cardiomyopathy, nominating druggable targets. Integrates human tissue, in vivo, and in vitro evidence.
Clinical Implications: Suggests BDH1 activation or LCN2 suppression as therapeutic strategies for diabetic cardiomyopathy; supports testing epigenetic modulators (e.g., H3K9bhb inhibitors) in cardiometabolic disease.
Key Findings
- BDH1 is reduced in diabetic human and db/db mouse hearts and in lipotoxic H9C2 cells.
- BDH1 deletion worsened, whereas AAV-mediated overexpression attenuated, diastolic dysfunction, apoptosis, fibrosis, and inflammation.
- BDH1 overexpression increased AcAc and decreased β‑OHB, reducing H3K9 β-hydroxybutyrylation at the LCN2 promoter, suppressing LCN2 and NF‑κB; LCN2 overexpression abrogated protection.
- The β-hydroxybutyrylation inhibitor A485 mitigated cardiac injury and reduced LCN2 in diabetic mice.
Methodological Strengths
- Multi-level evidence across human tissue, mouse models, and cardiomyocyte cell line
- Mechanistic causality supported by genetic gain/loss-of-function and pharmacologic inhibition
Limitations
- Preclinical models may not fully recapitulate human diabetic cardiomyopathy
- Long-term safety and translational efficacy of epigenetic modulation remain untested in humans
Future Directions: Validate BDH1/LCN2 axis in larger human cohorts; develop selective BDH1 activators or LCN2 inhibitors; test translational efficacy in large animals and assess sex-specific effects.
BACKGROUND: Diabetic cardiomyopathy (DbCM) is one of the common complications in diabetic patients, but there is no effective treatment for it up to now. Ketone bodies such as β-OHB have been widely reported to be beneficial for metabolic diseases including various diabetic complications. However, the role of ketone metabolism, especially the relevant enzymes, in the pathogenesis of DbCM is poorly understood. METHODS AND RESULTS: In this study, we firstly observed BDH1, the rate-limiting enzyme of ketone metabolism, was markedly diminished in cardiac tissues from db/db mice and diabetic patients, as well as in H9C2 cells treated with palmitic acid. Genetic deletion of BDH1 aggravated, whereas AAV-mediated BDH1 overexpression attenuated, the diastolic dysfunction and pathogenic progression including apoptosis, fibrosis and inflammation of hearts from db/db mice. Likewise, BDH1 knockdown promoted, whereas BDH1 overexpression reversed, the palmitic acid-induced lipotoxicity in H9C2 cells. Transcriptome analysis revealed that BDH1 negatively regulated LCN2 expression and LCN2 overexpression largely abrogated BDH1 overexpression-mediated myocardial protection in vitro and in vivo. Mechanistically, BDH1 overexpression reprogrammed ketone metabolism with increased AcAc and decreased β-OHB, thereby resulting in decreased β-hydroxybutyrylation of H3K9 on promoter region of LCN2, which repressed transcription of LCN2 and ultimately inhibited NF-κB activity through weakening interaction between NF-κB and RPS3. Furthermore, oral administration of β-hydroxybutyrylation inhibitor A485 to diabetic mice mitigated the cardiac injury concurrently with decreased expression of LCN2. CONCLUSION: Our results uncovered a novel mechanism whereby myocardial BDH1 ameliorates DbCM via epigenetic regulation of LCN2, which highlights the potential of BDH1/LCN2-based therapeutics in DbCM.
3. Incident type 2 diabetes and its risk factors in men and women aged 40-60 years from four sub-Saharan African countries: results from the AWI-Gen study.
In 6,553 middle-aged adults from South Africa, Kenya, Ghana, and Burkina Faso followed for 5–6 years (33,481 person-years), type 2 diabetes incidence was 14.6 per 1000 person-years, highest in South Africa and lowest in west Africa. Male sex, higher baseline glucose, family history, unemployment, hypertension, BMI, and waist circumference increased risk, while adequate physical activity lowered risk.
Impact: Provides robust, region-specific incidence and risk factor data to guide prevention policies and targeted interventions in sub-Saharan Africa.
Clinical Implications: Supports prioritizing screening and management of prediabetes, hypertension, and obesity, with promotion of physical activity and social determinants (e.g., employment) as intervention targets.
Key Findings
- Overall T2D incidence was 14.6 per 1000 person-years, highest in South Africa (21.8) and lowest in west Africa (5.5).
- Risk increased with higher baseline glucose, male sex, family history, unemployment, hypertension, BMI, and waist circumference.
- Adequate baseline physical activity was associated with lower incident T2D risk (adjusted OR ~0.87).
Methodological Strengths
- Prospective longitudinal design across four countries with standardized measurements
- Two-stage individual participant data meta-analysis to identify robust baseline predictors
Limitations
- Restricted to middle-aged adults (40–60 years), limiting generalizability to other age groups
- Potential heterogeneity in healthcare access and unmeasured confounding across sites
Future Directions: Extend to broader age ranges, evaluate targeted preventive programs, and integrate genetic and environmental data to refine risk stratification.
BACKGROUND: The incidence of type 2 diabetes in sub-Saharan Africa is expected to increase, but few longitudinal studies have characterised its risk factors. This study aimed to determine the incidence of type 2 diabetes over 33 481 person-years and identify its principal risk factors in middle-aged adults (ie, those aged 40-60 years) from four sub-Saharan African countries. METHODS: Longitudinal data were available from 6553 participants aged 40-60 years at baseline from study centres in South Africa, Kenya, Ghana, and Burkina Faso. Sociodemographic, behavioural, clinical, and biochemical data were collected at baseline and after an interval of 5-6 years. The prevalence of type 2 diabetes was determined at each timepoint and diabetes incidence was calculated. A two-stage individual participant data meta-analysis was used to identify baseline risk factors for incident diabetes. FINDINGS: The overall incidence of type 2 diabetes was 14·6 (95% CI 13·4-16·0) cases per 1000 person-years. The incidence was highest in South Africa with 21·8 (19·5-24·4) cases per 1000 person-years, and lowest in west Africa with 5·5 (4·4-6·9) cases per 1000 person-years. Baseline glucose (adjusted odds ratio 1·37; 95% CI 1·16-1·42), being male (1·32; 1·12-1·54), family history of type 2 diabetes (1·22; 1·01-1·46), unemployment (1·19; 1·03-1·37), hypertension (1·21; 1·01-1·45), BMI (1·03; 1·02-1·04), and waist circumference (1·02; 1·01-1·03), were associated with a higher risk of incident type 2 diabetes, while adequate baseline physical activity (0·87; 0·76-1·00) was associated with lower risk. INTERPRETATION: The high incidence of type 2 diabetes in this middle-aged sub-Saharan Africa population is influenced by several modifiable risk factors that should inform interventions to mitigate the disease burden. FUNDING: National Institutes of Health, Department of Science and Innovation (South Africa), and the South African Medical Research Council.