Lise Bathum

Hyperlipidemia is a well-established risk factor for cardiovascular disease and mortality. Recently, remnant cholesterol has been introduced as an important risk factor. This study explores the association between levels of remnant cholesterol, compared to the traditional lipid parameters (total cholesterol, low-density lipoprotein (LDL-C), high-density lipoprotein (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C) and triglycerides), and all-cause mortality in a population from general practice. Additionally, the impact of lipid-lowering treatment was evaluated. Observational cohort study based on the first lipid panel measurement from 327,347 patients from general practice in the Capital Region of Denmark between 2001 and 2018. LDL-C was calculated using the Friedewald equation. Patients with diagnoses or medical treatments that affected lipid levels were excluded. Cox proportional hazards models with restricted cubic splines were used to evaluate the association between all-cause mortality and lipid levels. A total of 34,014 patients died during the study. In an analysis censoring individuals receiving lipid lowering treatment after the lipid measurement, remnant cholesterol increased all-cause mortality risk linearly, with a hazard ratio (HR) of 1.6 (95% CI: 1.4; 1.7) at 3 mmol/L compared to a reference level of 0.9 mmol/L. Total cholesterol showed a U-shaped relationship with all-cause mortality with a HR of 2.5 (95% CI: 2.3; 2.7) at 2.5 mmol/L and 1.7 (95% CI: 1.6; 1.9) at 9 mmol/L (reference level 5 mmol/L). LDL-C and non-HDL-C exhibited a very similar U-shaped pattern. HDL-C also showed a U-shaped curve with a HR of 1.7 (95% CI: 1.6; 1.9) at 0.5 mmol/L and 1.4 (95% CI: 1.3; 1.5) at 3.5 mmol/L (reference level 1 mmol/L). The mortality risk related to triglycerides increased with rising triglyceride level, with a HR of 1.5 (95% CI: 1.3; 1.6) at 4.5 mmol/L (reference level 2 mmol/L). In this study, high levels of all the six lipids as well as low levels of total cholesterol, LDL-C, non-HDL-C and HDL-C were associated with higher all-cause mortality in a primary health care population. Further research is needed, to consider if the current lipid lowering guidelines are appropriate and if more focus on remnant cholesterol levels should be applied.

Long-term preventive treatment such as treatment with statins should be reassessed among patients approaching end of life. The aim of the study was to describe the rate of discontinuation of statin treatment and factors associated with discontinuation in the 6 months before death. This study is a retrospective cohort study using national registers and blood test results from primary health care patients. Patients in the Copenhagen municipality, Denmark who died between 1997 and 2018 and were statin users during the 10-year period before death were included. We calculated the proportion who remained statin users in the 6-month period before death. Factors associated with discontinuation were tested using logistic regression. A total of 55,591 decedents were included. More patients continued treatment (64%, n = 35,693) than discontinued (36%, n = 19,898) the last 6 months of life. The 70 and 80 age groups had the lowest odds of discontinuing compared to the 90 (OR 1.59, 95% CI 0.93–2.72) and 100 (OR 3.11, 95% CI 2.79–3.47) age groups. Increasing comorbidity score (OR 0.89, 95% CI 0.87; 0.90 per 1-point increase) and use of statins for secondary prevention (OR 0.89, 95% CI 0.85; 0.93) reduced the likelihood of discontinuation as did a diagnosis of dementia, heart failure, or cancer. A substantial portion of patients continued statin treatment near end of life. Efforts to promote rational statin use and discontinuation are required among patients with limited life expectancy, including establishing clear, practical recommendations about statin discontinuation, and initiatives to translate recommendations into clinical practice.

Background: In obesity, which is a major contributor to insulin resistance and diabetes, the circulating level of S100A8/A9 (calprotectin) is elevated and declines after Roux-en-Y gastric bypass surgery (RYGB). However, studies on S100A8/A9 and the pathophysiological mechanisms in insulin resistance and diabetes are few and contradictory. Methods: We studied 48 subjects who underwent RYGB, comprising a non-diabetic control group and two diabetic groups in whom diabetes either regressed or persisted, 6-12 months post-surgically. S100A8/A9, interleukin 6 (IL-6) as well as other inflammatory and diabetes-related markers were measured pre- and post-surgically. Results: Significant and similar decreases of BMI were found in all groups. S100A8/A9 and IL-6 decreased significantly in the group with diabetes remission and in the control group, but not in the group with persistent diabetes. The relative changes in S100A8/A9 and IL-6 correlated significantly (r = 0.905, p = 0.005) only in the group with persistent diabetes. In contrast, leukocyte count and C-reactive protein correlated significantly to S100A8/A9 only in the control group. Conclusion: Our study is suggestive of S100A8/A9 and IL-6 being related to a persistent diabetes status post-surgically and of different pathophysiological mechanisms being involved in the post-surgical changes in the three groups, despite similar decreases in BMI.

Although the ApoE gene has been intensively studied in aging research, most of the studies conducted so far have been based on the traditional case–control design with subjects consisting of young...

Summary: In this paper, we apply logistic regression models to measure genetic association with human survival for highly polymorphic and pleiotropic genes. By modelling genotype frequency as a function of age, we introduce a logistic regression model with polytomous responses to handle the polymorphic situation. Genotype and allele‐based parameterization can be used to investigate the modes of gene action and to reduce the number of parameters, so that the power is increased while the amount of multiple testing minimized. A binomial logistic regression model with fractional polynomials is used to capture the age‐dependent or antagonistic pleiotropic effects. The models are applied to HFE genotype data to assess the effects on human longevity by different alleles and to detect if an age‐dependent effect exists. Application has shown that these methods can serve as useful tools in searching for important gene variations that contribute to human aging and longevity.