Personalised Approach for Obesity Management: Integrating Adiposity Measures with Biomarkers
Explore how incorporating biomarkers enhances risk assessment in obesity management to achieve low-risk states for type 2 diabetes and cardiovascular diseases. The study analyzes the relationship between BMI, WHtR, and biomarker levels in a cohort of over 280,000 adults using Cox proportional hazards models.
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A personalised treat-to-target approach for obesity management: Integrating adiposity measures with biomarker levels to reach low-risk states of incident T2D and ASCVD Luca Busetto,1* Camilla S. Morgen2Maria Overvad,2Silvia Capucci,2 Ricardo Reynoso,3Rafael Bravo,2Volker Schnecke2 1Department of Medicine, University of Padova, Padova, Italy 2Novo Nordisk A/S, S borg, Denmark 3Novo Nordisk Health Care, Z rich, Switzerland This analysis was sponsored by Novo Nordisk A/S. Medical writing support was provided by Roxanna Munir, PhD, of Apollo, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice (GPP) guidelines (ismpp.org/gpp-2022). Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
I N CLU D E D I N P O S T E R Background and aims We previously demonstrated that incorporating WHtR in addition to BMI into risk models yields better estimates, particularly for the risk of T2D and ASCVD1 A BMI of 27 kg/m2and WHtR of 0.53 were suggested as potential treatment targets, indicated by an acceptably low risk of ORCs This study aims to investigate the inclusion of biomarkers into the assessment of progress towards a low-risk state during obesity management ORC, obesity-related complications; T2D, type 2 diabetes; WHtR, waist-to-height ratio. Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
I N CLU D E D I N P O S T E R 3 Methods We identified a cohort of 287,976 adult individuals from the CPRD Aurum database (containing UK primary care electronic medical records) with data on BMI, WHtR, SBP, DBP and levels of total cholesterol, HDL cholesterol and triglycerides Utilising Cox proportional hazards models, we estimated the 10-year risk of T2D and ASCVD and explored the relationship between BMI, WHtR, biomarker levels and comorbidity risk ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index; CPRD, Clinical Practice Research Datalink; DBP, diastolic blood pressure; HDL, high-density lipoprotein; SBP, systolic blood pressure; T2D, type 2 diabetes; WHtR, waist-to-height ratio. Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
A D D I T I ON A L I N F O R M A T I O N Baseline characteristics and biomarker levels Total Overweight (N=148,295) 41.3 / 58.7 Obesity I (N=84,813) 45.0 / 55.0 Obesity II (N=34,683) 54.6 / 45.4 Obesity III (N=20,185) 62.6 / 37.4 Characteristic/biomarker (N=287,976) 45.5 / 54.5 Women/Men, % Baseline characteristics, median (IQR) Age, years BMI, kg/m2 WtHR Follow-up, years Biomarker levels during baseline year, median (IQR) SBP, mmHg DBP, mmHg Total cholesterol, mmol/L HDL cholesterol, mmol Triglycerides, mmol/L Prevalence of comorbidities at start of follow-up, % Type 2 diabetes Hypertension Dyslipidaemia ASCVD Diagnoses during follow-up per 1,000 patient-years Type 2 diabetes ASCVD 50 (44, 55) 29.8 (27.3, 33.6) 27.3 (26.2, 28.6) 32.0 (30.9, 33.3) 36.9 (35.9, 38.3) 43.4 (41.4, 46.6) 0.59 (0.54, 0.64) 0.55 (0.52, 0.58) 0.61 (0.58, 0.64) 0.68 (0.64, 0.71) 0.75 (0.71, 0.80) 10.0 (7.4, 11.9) 9.9 (7.4, 11.6) 10.1 (7.5, 12.0) 50 (44, 55) 50 (44, 55) 50 (44, 55) 49 (43, 55) 10.2 (7.3, 12.3) 10.3 (7.0, 12.3) 130 (121, 140) 81 (75, 87) 5.2 (4.5, 5.9) 1.2 (1.1, 1.5) 1.5 (1.1, 2.2) 129 (120, 138) 80 (74, 86) 5.3 (4.6, 6.0) 1.3 (1.1, 1.6) 1.4 (1.0, 2.0) 132 (123, 141) 82 (76, 88) 5.2 (4.5, 6.0) 1.2 (1.0, 1.4) 1.6 (1.1, 2.3) 134 (125, 143) 83 (78, 89) 5.1 (4.4, 5.8) 1.2 (1.0, 1.4) 1.7 (1.2, 2.4) 136 (127, 145) 84 (78, 90) 4.9 (4.2, 5.6) 1.2 (1.0, 1.4) 1.7 (1.2, 2.3) 17.9 29.2 19.4 5.7 10.5 20.7 14.8 4.4 20.3 32.9 22.2 6.6 30.9 43.5 27.0 8.0 40.2 51.3 29.3 8.3 13.5 9.0 7.7 7.7 17.0 9.9 27.4 10.7 36.4 11.4 ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index; DBP, diastolic blood pressure; HDL, high-density lipoprotein; IQR, interquartile range; WtHR, waist-to-height ratio. Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
I N CLU D E D I N P O S T E R Covariate distributions to ORC risk estimates ASCVD Type 2 diabetes BMI (5 units) BMI (5 units) WHtR (0.05 units) WHtR (0.05 units) Sex (male) Sex (male) SBP (5 mm Hg) SBP (5 mm Hg) DBP (5 mm Hg) DBP (5 mm Hg) Total cholesterol (0.5 mmol/L) Total cholesterol (0.5 mmol/L) HDL cholesterol (0.2 mmol/L) HDL cholesterol (0.2 mmol/L) Triglycerides (0.5 mmol/L) Triglycerides (0.5 mmol/L) Type 2 diabetes Hypertension Hypertension Dyslipidaemia Dyslipidaemia 1.8 0.8 1.0 1.5 1.0 0.8 Hazard ratio (95% CI) Hazard ratio (95% CI) Biomarker levels can independentlycomplementBMI and WHtR in modulating the risk of T2D and ASCVD Associations between model parameters and ORC risk from Cox proportional hazards models. ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index; CI, confidence interval; DBP, diastolic blood pressure; HDL, high-density lipoprotein; SBP, systolic blood pressure; T2D, type 2 diabetes; WHtR, waist-to-height ratio. Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
I N CLU D E D I N P O S T E R Reaching target ORC risk The risk for T2D and ASCVD can be modulated by different combinations of BMI, WHtR and biomarker levels 10-year T2D risk for 50-year-old man 10-year ASCVD risk for 50-year-old man with BMI 30 kg/m2 10-year ASCVD risk for 50-year-old men with BMI 30 kg/m2 WHtR 0.58 0.60 0.55 0.58 0.51 0.48 4.8 5.7 27 32 Target risk of 6.5% can be achieved with a WHtR of 0.55 and a total cholesterol level of 4.8 mmol/L Target risk of 5.4% can be achieved with a BMI of 27 kg/m2 and WHtR of 0.51 Target risk of 6.5% can be achieved with a WHtR of 0.48 The target ORC risk is based on the 10-year ORC incidence for individuals with same age and gender and BMI <30 kg/m2. ASCVD, atherosclerotic cardiovascular disease; ORC, obesity-related complications; WHtR, waist-to-height ratio. Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
I N CLU D E D I N P O S T E R Discussion and conclusion If information on biomarkers is available, integrating changes in biomarker levels during weight loss represents a stride towards a personalised treatment target in obesity management Our findings suggest that supplementaryinterventions (e.g. lipid-lowering) may contribute to achieving a low risk for incident T2D or ASCVD, despite some patients not meeting adiposity-based treatment targets ASCVD, atherosclerotic cardiovascular disease; T2D, type 2 diabetes. Busetto L, et al. Presented at the European Congress on Obesity (ECO), 11 14 May 2025, Malaga, Spain.
