Obesity and healthcare resource utilization: results from Clinical Practice Research Database (CPRD).

BACKGROUND/OBJECTIVES: The economic burden of obesity and type 2 diabetes (T2D) rises with increasing prevalence. This study estimates the association between obesity, healthcare resource utilization (HCRU) and associated costs in individuals with/without T2D. SUBJECTS/METHODS: This observational cohort study used the United Kingdom Clinical Practice Research Datalink data. Between 1 January 2011 and 31 December 2015, total HCRU costs and individual component costs (hospitalizations, general practitioner contacts, prescriptions) were calculated for individuals linked to the Hospital Episodes Statistics database with/without T2D with normal weight, overweight, class I, II, III obesity. RESULTS: A total of 396,091 individuals were included. Increasing body mass index (BMI) was associated with increased HCRU costs. At each BMI category, costs were greater for individuals with than without T2D. Relative to normal BMI, increasing BMI was positively associated with increased HCRU costs, with similar magnitude regardless of T2D. The total HCRU cost for an individual with class III obesity was 1.4-fold (£3,695) greater than for normal weight. CONCLUSION: In the United Kingdom, HCRU costs were positively associated with increasing BMI, irrespective of T2D status. The combination of T2D and obesity was associated with higher HCRU costs compared with individuals of the same BMI, without T2D. These findings suggest that prioritizing weight management programmes focused specifically on individuals with obesity and T2D may be more cost-effective than for those with obesity alone.

Obesity and healthcare resource utilization: comparative results from the UK and the USA.

Objectives: To estimate the differences between individuals with and without obesity on healthcare resource utilization using two large electronic medical record databases. Methods: Data from the UK Clinical Practice Research Datalink and US General Electric Centricity database of adults (≥18 years) with registration date before 01/01/2010. Differences between individuals with and without obesity on 5-year rates of Primary Care Physician (PCP) contacts, prescriptions and hospitalizations were analysed. Results: The study contained 1,878,017 UK and 4,414,883 US individuals. Compared with body mass index (BMI) (18.5-24.9 kg m-2), significant (p < 0.0001) increases in healthcare usage were observed with increasing BMI. Individuals with BMI 30-34.9 kg m-2 had higher PCP contact rate (rate ratios [RR] 1.27 and 1.28 for UK and USA, respectively), higher prescription rate (RR 1.61 and 1.51) and higher hospitalization rate (RR 1.10 and 1.13) than individuals with BMI 18.5-24.9 kg m-2. Individuals with BMI >40 kg m-2 also had higher PCP contact rate (RR 1.56 and 1.64), prescription rate (RR 2.48 and 2.14) and hospitalization rate (RR 1.27 and 1.30) than individuals with BMI 18.5-24.9 kg m-2. Conclusions: The utilization of healthcare resources is significantly higher in individuals with obesity. A similar trend was observed in both the UK and US cohorts.

A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals.

BACKGROUND: In Europeans, 45 genetic risk variants for coronary artery disease (CAD) have been identified in genome-wide association studies. We constructed a genetic risk score (GRS) of these variants to estimate the effect on incidence and clinical predictability of myocardial infarction (MI) and CAD. METHODS: Genotype was available from 6041 Danes. An unweighted GRS was constructed by making a summated score of the 45 known genetic CAD risk variants. Registries provided information (mean follow-up = 11.6 years) on CAD (n = 374) and MI (n = 124) events. Cox proportional hazard estimates with age as time scale was adjusted for sex, BMI, type 2 diabetes mellitus and smoking status. Analyses were also stratified either by sex or median age (below or above 45 years of age). We estimated GRS contribution to MI prediction by assessing net reclassification index (NRI) and integrated discrimination improvement (IDI) added to the European SCORE for 10-year MI risk prediction. RESULTS: The GRS associated significantly with risk of incident MI (allele-dependent hazard ratio (95%CI): 1.06 (1.02-1.11), p = 0.01) but not with CAD (p = 0.39). Stratification revealed association of GRS with MI in men (1.06 (1.01-1.12), p = 0.02) and in individuals above the median of 45.11 years of age (1.06 (1.00-1.12), p = 0.03). There was no interaction between GRS and gender (p = 0.90) or age (p = 0.83). The GRS improved neither NRI nor IDI. CONCLUSION: The GRS of 45 GWAS identified risk variants increase the risk of MI in a Danish cohort. The GRS did not improve NRI or IDI beyond the performance of conventional European SCORE risk factors.

The effect of GWAS identified BMI loci on changes in body weight among middle-aged Danes during a five-year period.

OBJECTIVE: Genome-wide association studies have identified genetic variants associating with BMI, however, it is un-clarified whether the same variants also influence body weight fluctuations. METHODS: Among 3,982 adult individuals that attended both a baseline and a five-year follow-up examination in the Danish Inter99 intervention study, a genetic risk score (GRS) was constructed based on 30 BMI variants to address whether it is associated with body weight changes. Moreover, it was examined whether the effect of lifestyle changes was modulated by the GRS. RESULTS: The GRS associated strongly with baseline body weight, with a per risk allele increase of 0.45 (0.33-0.58) kg (P = 2.7 × 10(-12) ), corresponding to a body weight difference of 3.41 (2.21-4.60) kg comparing the highest (≥ 30 risk alleles) and lowest (≤ 26 risk alleles) risk allele tertile. No association was observed with changes in body weight during the five years. Changes in lifestyle, including physical activity, diet and smoking habits associated strongly with body weight changes, however, no interactions with the GRS was observed. CONCLUSION: The GRS associated with body weight cross-sectionally, but not with changes over a five-year period. Body weight changes were influenced by lifestyle changes, however, independently of the GRS.