A cost analysis of intensified vs conventional multifactorial therapy in individuals with type 2 diabetes: a post hoc analysis of the Steno-2 study.

AIMS/HYPOTHESIS: Long-term follow-up of the Steno-2 study demonstrated that intensified multifactorial intervention increased median lifespan by 7.9 years and delayed incident cardiovascular disease by a median of 8.1 years compared with conventional multifactorial intervention during 21.2 years of follow-up. In this post hoc analysis of data from the Steno-2 study, we aimed to study the difference in direct medical costs associated with conventional vs intensified treatment. METHODS: In 1993, 160 Danish individuals with type 2 diabetes and microalbuminuria were randomised to conventional or intensified multifactorial target-driven intervention for 7.8 years. Information on direct healthcare costs was retrieved from health registries, and the costs in the two groups of participants were compared by bootstrap t test analysis. RESULTS: Over 21.2 years of follow-up, there was no difference in total direct medical costs between the intensified treatment group, €12,126,900, and the conventional treatment group, €11,181,700 (p = 0.48). The mean cost per person-year during 1996-2014 was significantly lower in the intensified treatment group (€8725 in the intensive group and €10,091 in the conventional group, p = 0.045). The main driver of this difference was reduced costs associated with inpatient admissions related to cardiovascular disease (p = 0.0024). CONCLUSIONS/INTERPRETATION: Over a follow-up period of 21.2 years, we found no difference in total costs and reduced cost per person-year associated with intensified multifactorial treatment for 7.8 years compared with conventional multifactorial treatment. Considering the substantial gain in life-years and health benefits achieved with intensified treatment, we conclude that intensified multifaceted intervention in high-risk individuals with type 2 diabetes seems to be highly feasible when balancing healthcare costs and treatment benefits in a Danish healthcare setting.

Cohort description: The Danish study of Functional Disorders.

The Danish study of Functional Disorders (DanFunD) cohort was initiated to outline the epidemiology of functional somatic syndromes (FSS) and is the first larger coordinated epidemiological study focusing exclusively on FSS. FSS are prevalent in all medical settings and can be defined as syndromes that, after appropriate medical assessment, cannot be explained in terms of a conventional medical or surgical disease. FSS are frequent and the clinical importance varies from vague symptoms to extreme disability. No well-described medical explanations exist for FSS, and how to delimit FSS remains a controversial topic. The specific aims with the cohort were to test delimitations of FSS, estimate prevalence and incidence rates, identify risk factors, delimitate the pathogenic pathways, and explore the consequences of FSS. The study population comprises a random sample of 9,656 men and women aged 18-76 years from the general population examined from 2011 to 2015. The survey comprises screening questionnaires for five types of FSS, ie, fibromyalgia, whiplash-associated disorder, multiple chemical sensitivity, irritable bowel syndrome, and chronic fatigue syndrome, and for the unifying diagnostic category of bodily distress syndrome. Additional data included a telephone-based diagnostic interview assessment for FSS, questionnaires on physical and mental health, personality traits, lifestyle, use of health care services and social factors, and a physical examination with measures of cardiorespiratory and morphological fitness, metabolic fitness, neck mobility, heart rate variability, and pain sensitivity. A biobank including serum, plasma, urine, DNA, and microbiome has been established, and central registry data from both responders and nonresponders are similarly available on morbidity, mortality, reimbursement of medicine, heath care use, and social factors. A complete 5-year follow-up is scheduled to take place from year 2017 to 2020, and further reexaminations will be planned. Several projects using the DanFunD data are ongoing, and findings will be published in the coming years.

Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial.

BACKGROUND: Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, the concentrations of which can be affected by food intake. We hypothesised that metabolic profiles of urine samples developed under controlled feeding conditions reflect dietary intake and can be used to model and classify dietary patterns of free-living populations. METHODS: In this randomised, controlled, crossover trial, we recruited healthy volunteers (aged 21-65 years, BMI 20-35 kg/m2) from a database of a clinical research unit in the UK. We developed four dietary interventions with a stepwise variance in concordance with the WHO healthy eating guidelines that aim to prevent non-communicable diseases (increase fruits, vegetables, whole grains, and dietary fibre; decrease fats, sugars, and salt). Participants attended four inpatient stays (72 h each, separated by at least 5 days), during which they were given one dietary intervention. The order of diets was randomly assigned across study visits. Randomisation was done by an independent investigator, with the use of opaque, sealed, sequentially numbered envelopes that each contained one of the four dietary interventions in a random order. Participants and investigators were not masked from the dietary intervention, but investigators analysing the data were masked from the randomisation order. During each inpatient period, urine was collected daily over three timed periods: morning (0900-1300 h), afternoon (1300-1800 h), and evening and overnight (1800-0900 h); 24 h urine samples were obtained by pooling these samples. Urine samples were assessed by proton nuclear magnetic resonance (1H-NMR) spectroscopy, and diet-discriminatory metabolites were identified. We developed urinary metabolite models for each diet and identified the associated metabolic profiles, and then validated the models using data and samples from the INTERMAP UK cohort (n=225) and a healthy-eating Danish cohort (n=66). This study is registered with ISRCTN, number ISRCTN43087333. FINDINGS: Between Aug 13, 2013, and May 18, 2014, we contacted 300 people with a letter of invitation. 78 responded, of whom 26 were eligible and invited to attend a health screening. Of 20 eligible participants who were randomised, 19 completed all four 72 h study stays between Oct 2, 2013, and July 29, 2014, and consumed all the food provided. Analysis of 1H-NMR spectroscopy data indicated that urinary metabolic profiles of the four diets were distinct. Significant stepwise differences in metabolite concentrations were seen between diets with the lowest and highest metabolic risks. Application of the derived metabolite models to the validation datasets confirmed the association between urinary metabolic and dietary profiles in the INTERMAP UK cohort (p<0·0001) and the Danish cohort (p<0·0001). INTERPRETATION: Urinary metabolite models developed in a highly controlled environment can classify groups of free-living people into consumers of diets associated with lower or higher non-communicable disease risk on the basis of multivariate metabolite patterns. This approach enables objective monitoring of dietary patterns in population settings and enhances the validity of dietary reporting. FUNDING: UK National Institute for Health Research and UK Medical Research Council.

Mechanism-based population modelling for assessment of L-cell function based on total GLP-1 response following an oral glucose tolerance test.

GLP-1 is an insulinotropic hormone that synergistically with glucose gives rise to an increased insulin response. Its secretion is increased following a meal and it is thus of interest to describe the secretion of this hormone following an oral glucose tolerance test (OGTT). The aim of this study was to build a mechanism-based population model that describes the time course of total GLP-1 and provides indices for capability of secretion in each subject. The goal was thus to model the secretion of GLP-1, and not its effect on insulin production. Single 75 g doses of glucose were administered orally to a mixed group of subjects ranging from healthy volunteers to patients with type 2 diabetes (T2D). Glucose, insulin, and total GLP-1 concentrations were measured. Prior population data analysis on measurements of glucose and insulin were performed in order to estimate the glucose absorption rate. The individual estimates of absorption rate constants were used in the model for GLP-1 secretion. Estimation of parameters was performed using the FOCE method with interaction implemented in NONMEM VI. The final transit/indirect-response model obtained for GLP-1 production following an OGTT included two stimulation components (fast, slow) for the zero-order production rate. The fast stimulation was estimated to be faster than the glucose absorption rate, supporting the presence of a proximal-distal loop for fast secretion from L: -cells. The fast component (st3) = 8.64·10⁻5 [mg⁻¹]) was estimated to peak around 25 min after glucose ingestion, whereas the slower component (st4 = 26.2·10⁻5 [mg⁻¹]) was estimated to peak around 100 min. Elimination of total GLP-1 was characterised by a first-order loss. The individual values of the early phase GLP-1 secretion parameter (st3) were correlated (r = 0.52) with the AUC(0-60 min.) for GLP-1. A mechanistic population model was successfully developed to describe total GLP-1 concentrations over time observed after an OGTT. The model provides indices related to different mechanisms of subject abilities to secrete GLP-1. The model provides a good basis to study influence of different demographic factors on these components, presented mainly by indices of the fast- and slow phases of GLP-1 response.

Design of association studies with pooled or un-pooled next-generation sequencing data.

Most common hereditary diseases in humans are complex and multifactorial. Large-scale genome-wide association studies based on SNP genotyping have only identified a small fraction of the heritable variation of these diseases. One explanation may be that many rare variants (a minor allele frequency, MAF <5%), which are not included in the common genotyping platforms, may contribute substantially to the genetic variation of these diseases. Next-generation sequencing, which would allow the analysis of rare variants, is now becoming so cheap that it provides a viable alternative to SNP genotyping. In this paper, we present cost-effective protocols for using next-generation sequencing in association mapping studies based on pooled and un-pooled samples, and identify optimal designs with respect to total number of individuals, number of individuals per pool, and the sequencing coverage. We perform a small empirical study to evaluate the pooling variance in a realistic setting where pooling is combined with exon-capturing. To test for associations, we develop a likelihood ratio statistic that accounts for the high error rate of next-generation sequencing data. We also perform extensive simulations to determine the power and accuracy of this method. Overall, our findings suggest that with a fixed cost, sequencing many individuals at a more shallow depth with larger pool size achieves higher power than sequencing a small number of individuals in higher depth with smaller pool size, even in the presence of high error rates. Our results provide guidelines for researchers who are developing association mapping studies based on next-generation sequencing.

Past and current body size affect validity of reported energy intake among middle-aged Danish men.

Our objectives were to estimate the degree of misreporting energy intake (EI) and analyze associations with previous BMI, current BMI, or both. The study was part of the Adiposity and Genetics Study follow-up study including 309 Danish men (age 40-65 y) originally sampled from the obligatory draft board examination. Height and weight were measured at the mean ages of 20 (draft board), 33, 44, and 49 y (current age). Obesity was categorized as BMI >or= 31 kg/m(2). Dietary intake for 7 d and physical activity (PA) level (PAL) were self-reported. Resting metabolic rate (RMR) was measured in a ventilated hood system. By comparing EI with energy expenditure and assuming energy balance, reporting accuracy (RA) was estimated as EI/(RMR.PAL). A plausibility interval was calculated to encompass specific variation components of EI, RMR, and PAL; the specific 95% plausibility interval was 1.00 +/- 0.35. Participants were categorized as underreporters (RA 1.35) of EI. The relation between RA and BMI was studied through linear regression analysis. Overall, the RA was (mean +/- SE) 0.76 +/- 0.01. Of 309 participants, 35% underreported and 7% overreported. Whether stratified for current BMI or draft board BMI, the obese men were more likely to underreport than those who were not obese. Among those currently not obese, underreporting was more prevalent among those who were obese at the draft board examination (44%) than among those who were not (21%). Regression analysis showed that both previous and current BMI and their combination were significantly associated with RA. Thus, underreporting of dietary intake seems to be associated with not only current BMI but also with current BMI in combination with previous BMI.

Cost-effectiveness of intensified versus conventional multifactorial intervention in type 2 diabetes: results and projections from the Steno-2 study.

OBJECTIVE: To assess the cost-effectiveness of intensive versus conventional therapy for 8 years as applied in the Steno-2 study in patients with type 2 diabetes and microalbuminuria. RESEARCH DESIGN AND METHODS: A Markov model was developed to incorporate event and risk data from Steno-2 and account Danish-specific costs to project life expectancy, quality-adjusted life expectancy (QALE), and lifetime direct medical costs expressed in year 2005 Euros. Clinical and cost outcomes were projected over patient lifetimes and discounted at 3% annually. Sensitivity analyses were performed. RESULTS: Intensive treatment was associated with increased life expectancy, QALE, and lifetime costs compared with conventional treatment. Mean +/- SD undiscounted life expectancy was 18.1 +/- 7.9 years with intensive treatment and 16.2 +/- 7.3 years with conventional treatment (difference 1.9 years). Discounted life expectancy was 13.4 +/- 4.8 years with intensive treatment and 12.4 +/- 4.5 years with conventional treatment. Lifetime costs (discounted) for intensive and conventional treatment were euro45,521 +/- 19,697 and euro41,319 +/- 27,500, respectively (difference euro4,202). Increased costs with intensive treatment were due to increased pharmacy and consultation costs. Discounted QALE was 1.66 quality-adjusted life-years (QALYs) higher for intensive (10.2 +/- 3.6 QALYs) versus conventional (8.6 +/- 2.7 QALYs) treatment, resulting in an incremental cost-effectiveness ratio of euro2,538 per QALY gained. This is considered a conservative estimate because accounting prescription of generic drugs and capturing indirect costs would further favor intensified therapy. CONCLUSIONS: From a health care payer perspective in Denmark, intensive therapy was more cost-effective than conventional treatment. Assuming that patients in both arms were treated in a primary care setting, intensive therapy became dominant (cost- and lifesaving).

A Bayesian multilocus association method: allowing for higher-order interaction in association studies.

For most common diseases with heritable components, not a single or a few single-nucleotide polymorphisms (SNPs) explain most of the variance for these disorders. Instead, much of the variance may be caused by interactions (epistasis) among multiple SNPs or interactions with environmental conditions. We present a new powerful statistical model for analyzing and interpreting genomic data that influence multifactorial phenotypic traits with a complex and likely polygenic inheritance. The new method is based on Markov chain Monte Carlo (MCMC) and allows for identification of sets of SNPs and environmental factors that when combined increase disease risk or change the distribution of a quantitative trait. Using simulations, we show that the MCMC method can detect disease association when multiple, interacting SNPs are present in the data. When applying the method on real large-scale data from a Danish population-based cohort, multiple interactions are identified that severely affect serum triglyceride levels in the study individuals. The method is designed for quantitative traits but can also be applied on qualitative traits. It is computationally feasible even for a large number of possible interactions and differs fundamentally from most previous approaches by entertaining nonlinear interactions and by directly addressing the multiple-testing problem.