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Background and aims: Unfiltered coffee contains high concentrations of cholesterol-raising diterpenes. We aimed to measure the levels of diterpenes in machine coffee. Methods and results: Coffee samples were collected from Swedish workplaces and compared with home-made coffee brews. Concentrations of cafestol and kahweol were measured by liquid chromatography-mass spectrometry. The median (range) cafestol and kahweol concentrations were 176 (24-444) mg/L and 142 (18-434) mg/L for brewing machines (n = 11), 8 (2-343) mg/L and 7 (2-288) mg/L for liquid-model machines (n = 3), and 12 (4-24) mg/L and 8 (3-19) mg/l for home-brewed, paper-filtered coffees (n = 5). Boiled coffee had high concentrations of cafestol and kahweol, 939 mg/L and 678 mg/L, but having it poured through a fabric filter reduced the concentrations to 28 and 21 mg/L. Other coffee brews (percolator, French press) contained intermediate levels of cafestol (similar to 90 mg/L) and kahweol (similar to 70 mg/L), with the exception of some espresso samples with high levels (up to 2447 mg/L cafestol). Conclusion: Most coffees from workplace brewing machines contain higher diterpene concentrations than paper-filtered coffee, but lower than unfiltered coffee. Intake of insufficiently filtered coffee during working hours could be an overlooked factor for cardiovascular health due to its effect on plasma cholesterol concentrations.

Background & aims

Continuous glucose monitoring (CGM) provides data on short-term glycemic variability (GV). GV is associated with adverse outcomes in individuals with diabetes. Whether GV is associated with cardiometabolic risk in individuals without diabetes is unclear. We systematically reviewed the literature to assess whether GV is associated with cardiometabolic risk markers or outcomes in individuals without diabetes.

Methods

Searches were performed in PubMed/Medline, Embase and Cochrane from inception through April 2022. Two researchers were involved in study selection, data extraction and quality assessment. Studies evaluating GV using CGM for >= 24 h were included. Studies in populations with acute and/or critical illness were excluded. Both narrative synthesis and meta -analyzes were performed, depending on outcome.

Results

Seventy-one studies were included; the majority were cross-sectional. Multiple measures of GV are higher in individuals with compared to without prediabetes and GV appears to be inversely associated with beta cell function. In contrast, GV is not clearly associated with insulin sensitivity, fatty liver disease, adiposity, blood lipids, blood pressure or oxidative stress. However, GV may be positively associated with the degree of atherosclerosis and cardiovascular events in individuals with coronary disease.

Conclusion

GV is elevated in prediabetes, potentially related to beta cell dysfunction, but less clearly associated with obesity or traditional risk factors. GV is associated with coronary atherosclerosis development and may predict cardiovascular events and type 2 diabetes. Prospective studies are warranted, investigating the predictive power of GV in relation to incident disease. GV may be an important risk measure also in individuals without diabetes. 

BACKGROUND: Fatty acids may influence lean tissue volume and skeletal muscle function. We previously reported in young lean participants that overfeeding polyunsaturated fat (PUFA) compared with saturated fat (SFA) induced greater lean tissue accumulation despite similar weight gain.

OBJECTIVE: In a double-blind randomized controlled trial (RCT), we aimed to investigate if the differential effects of overfeeding SFA and PUFA on lean tissue accumulation could be replicated in individuals with overweight, and identify potential determinants. Further, using substitution models, we investigated associations between SFA and PUFA levels with lean tissue volume, in a large population-based sample (UK Biobank).

METHODS: Sixty-one males and females with overweight (BMI 27.3 (interquartile range 25.4 to 29.3), age 43 (interquartile range 36 to 48)) were overfed SFA (palm oil) or n-6 PUFA (sunflower oil) for 8 weeks. Lean tissue was assessed by magnetic resonance imaging (MRI). We had access to n=13849 participants with data on diet, covariates and MRI measurements of lean tissue, as well as 9119 participants with data on circulating fatty acids, in the UK Biobank.

RESULTS: Body weight gain (mean±SD) was similar in PUFA (2.01±1.90 kg) and SFA (2.31±1.38 kg) groups. Lean tissue increased to a similar extent (0.54±0.93 L and 0.67±1.21 L for PUFA and SFA group, respectively, with a difference between groups of 0.07 (-0,21, 0,35)). We observed no differential effects on circulating amino acids, myostatin or interleukin-15 and no clear determinants of lean tissue accumulation. Similar non-significant results for SFA and PUFA were observed in UK Biobank, but circulating fatty acids demonstrated ambiguous and sex-dependent associations.

CONCLUSION: Overfeeding SFA or PUFA does not differentially affect lean tissue accumulation during 8 weeks in individuals with overweight. A lack of dietary fat type-specific effects on lean tissue is supported by specified substitution models in a large population-based cohort consuming their habitual diet.

TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02211612.

Background: It is unclear whether moderate differences in dietary carbohydrate quantity and quality influence plasma FAs in the lipogenic pathway in healthy adults.

Objectives: We investigated the effects of different carbohydrate quantities and quality on plasma palmitate concentrations (primary outcome) and other saturated and MUFAs in the lipogenic pathway.

Methods: Twenty healthy participants were randomly assigned, and 18 (50% women; age: 22-72 y; BMI: 18.2-32.7 kg/m2 and BMI was measured in kg/m2) started the cross-over intervention. During each 3-wk period (separated by a 1-wk washout period), 3 diets were consumed (all foods provided) in random order: low-carbohydrate (LC) (38% energy (E) carbohydrates, 25-35 g fiber/d, 0% E added sugars); high-carbohydrate/high-fiber (HCF) (53% E carbohydrates, 25-35 g fiber/d, 0% E added sugars); and high-carbohydrate/high-sugar (HCS) (53% E carbohydrates, 19-21 g fiber/d, 15% E added sugars). Individual FAs were measured proportionally to total FAs by GC in plasma cholesteryl esters, phospholipids, and TGs. False discovery rate-adjusted repeated measures ANOVA [ANOVA-false discovery rate (FDR)] was used to compare outcomes.

Results: The self-reported intakes of carbohydrates and added-and free sugars were; 30.6% E and 7.4% E in LC, 41.4% E and 6.9% E in HCF, and 45.7% E and 10.3% in HCS. Plasma palmitate did not differ between the diet periods (ANOVA FDR P > 0.43, n = 18). After HCS, myristate concentrations in cholesterol esters and phospholipids were >= 19% higher than LC and >= 22% higher than HCF (P = 0.005). After LC, palmitoleate in TG was 6% lower compared with HCF and 7% compared with HCS (P = 0.041). Body weight differed (<= 0.75 kg) between diets before FDR correction.

Conclusions: Different carbohydrate quantity and quality do not influence plasma palmitate concentrations after 3 wk in healthy Swedish adults, whereas myristate increased after the moderately higher intake of carbohydrate/high-sugar, but not carbohydrate/high-fiber. Whether plasma myristate is more responsive than palmitate to differences in carbohydrate intake requires further study, especially considering that participants deviated from the planned dietary targets.

Lifestyle management is the first line of treatment for moderately elevated blood lipids in healthy individuals. We investigated the effectiveness of providing food-based written advice for lowering low-density lipoprotein (LDL) cholesterol (intervention) or triglycerides (control) in a pragmatic randomized controlled trial with two parallel arms from 2018-2019 at a rural primary health care center. We sent feedback letters after 3 weeks and 6 months. Out of the 113 adult primary care patients randomized, 112 completed the study. There were no differences between the intervention and control groups for changes in LDL cholesterol after 3 weeks (mean +/- standard deviation -0.21 +/- 0.38 vs. -0.11 +/- 0.34 mmol/L, p = 0.45) or 6 months (-0.05 +/- 0.47 vs. 0.02 +/- 0.41 mmol/L, p = 0.70) (primary outcome). Following the advice to consume plant sterols and turmeric was associated with a reduction in LDL cholesterol after 3 weeks. Following the advice to consume less carbohydrates was associated with reduced triglycerides. In the intervention arm, 14 individuals (25%) reduced their LDL cholesterol by >= 10% after three weeks. Their reduction was attenuated but maintained after six months (-7.1 +/- 9.2% or -0.31 +/- 0.38 mmol/L, p = 0.01 compared with baseline). They differed only in higher adherence to the advice regarding turmeric. In conclusion, this undemanding intervention had little effect on blood lipids for most individuals.

Background Gout affects nearly 2 % of the population and is associated with repeated painful flares of arthritis. Preventive urate-lowering therapy is widely available, but only one third of patients receive adequate treatment. Lack of knowledge among healthcare professionals and patients within primary healthcare are implicated as partial explanations for this undertreatment. Nurse-led care has proved to be an effective model when treating patients with gout, but there is a need for more knowledge about factors that can be expected to influence the future implementation of such care. The aim of this study was to describe factors influencing existing gout care in primary healthcare and the conditions for a future implementation of nurse-led gout care based on national treatment recommendations. Methods In this qualitative study, focus group discussions with 56 nurses and physicians and individual interviews with eight managers were conducted at nine primary healthcare units in central Sweden. A deductive qualitative content analysis based on the main constructs of the framework Integrated Promoting Action on Research Implementation in Health Services was followed by an inductive analysis within the frames of the main constructs: innovation, recipients and context. Results Gout-related contacts with primary healthcare was described as being patient initiated, diagnostics was in some respects complex and nurse-led care was experienced as a favourable primary healthcare model in general (innovation). Gout was seen as a low-priority condition with acute flares and there was inadequate knowledge of gout, including preventive treatment (recipients). Primary healthcare was perceived as having a holistic but fragmented responsibility for gout care, recommendations against keeping waiting lists complicated follow-up appointments and a need for motivation and support when introducing new practices was emphasised (context). Conclusion In this study, investigating the perspective of professionals, several factors were found to influence existing gout care. It will be crucial to target these factors in the development of a future implementation strategy.

BACKGROUND: Underlying mechanism leading to impaired lung function are incompletely understood.

OBJECTIVES: To investigate whether protein profiling can provide novel insights into mechanisms leading to impaired lung function.

METHODS: We used four community-based studies (n = 2552) to investigate associations between 79 cardiovascular/inflammatory proteins and forced expiratory volume in 1 s percent predicted (FEV₁%) assessed by spirometry. We divided the cohorts into discovery and replication samples and used risk factor-adjusted linear regression corrected for multiple comparison (false discovery rate of 5%). We performed Mendelian randomization analyses using genetic and spirometry data from the UK Biobank (n = 421,986) to assess causality.

MEASUREMENTS AND MAIN RESULTS: In cross-sectional analysis, 22 proteins were associated with lower FEV₁% in both the discovery and replication sample, regardless of stratification by smoking status. The combined proteomic data cumulatively explained 5% of the variation in FEV₁%. In longitudinal analyses (n = 681), higher plasma levels of growth differentiation factor 15 (GDF-15) and interleukin 6 (IL-6) predicted a more rapid 5-year decline in lung function (change in FEV₁% per standard deviation of protein level -1.4, (95% CI, -2.5 to -0.3) for GDF-15, and -0.8, (95% CI, -1.5 to -0.2) for IL-6. Mendelian randomization analysis in UK-biobank provided support for a causal effect of increased GDF-15 levels and reduced FEV₁%.

CONCLUSIONS: Our combined approach identified GDF-15 as a potential causal factor in the development of impaired lung function in the general population. These findings encourage additional studies evaluating the role of GDF-15 as a causal factor for impaired lung function.

Aims: To systematically evaluate the evidence regarding the effects of foods on LDL cholesterol levels and to compare the findings with current guidelines. Data synthesis: From inception through June 2019, we searched PubMed, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials for guidelines, systematic reviews, and RCTs (for coffee intake only) of at least 13 days duration. Additionally, we searched Trip database for guidelines from 2009 through Oct 2019. Language was restricted to English. The strength of evidence was evaluated using The Grading of Recommendations Assessment, Devel-opment, and Evaluation (GRADE). A total of 37 guidelines, 108 systematic reviews, and 20 RCTs were included. With high evidence, foods high in unsaturated and low in saturated and trans fatty acids (e.g. rapeseed/canola oil), with added plant sterols/stanols, and high in soluble fiber (e.g. oats, barley, and psyllium) caused at least moderate (i.e. 0.20-0.40 mmol/L) reductions in LDL cholesterol. Unfiltered coffee caused a moderate to large increase. Soy protein, tomatoes, flaxseeds, and almonds caused small reductions. With moderate evidence, avocados and turmeric caused moderate to large reductions. Pulses, hazelnuts, walnuts, high -fiber/ wholegrain foods, and green tea caused small to moderate reductions, whereas sugar caused a small increase. Other identified foods were either neutral or had low or very low evidence regarding their effects. Conclusions: Several foods distinctly modify LDL cholesterol levels. The results may aid future guidelines and dietary advice for hypercholesterolemia. (c) 2021 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Background: Saturated fat (SFA) has consistently been shown to increase liver fat, but the response appears variable at the individual level. Phenotypic and genotypic characteristics have been demonstrated to modify the hypercholesterolemic effect of SFA but it is unclear which characteristics that predict liver fat accumulation in response to a hypercaloric diet high in SFA.

Objective: To identify predictors of liver fat accumulation in response to an increased intake of SFA.

Design: We pooled our two previously conducted double-blind randomized trials (LIPOGAIN and LIPOGAIN-2, clinicaltrials.gov NCT01427140 and NCT02211612) and used data from the n = 49 metabolically healthy men (n = 32) and women (n = 17) randomized to a hypercaloric diet through addition of SFA-rich muffins for 7-8 weeks. Associations between clinical and metabolic variables at baseline and changes in liver fat during the intervention were analyzed using Spearman rank correlation. Linear regression was used to generate a prediction model.

Results: Liver fat increased by 33% (IQR 5.4-82.7%; P < 0.0001) in response to excess energy intake and this was not associated (r = 0.17, P = 0.23) with the increase in body weight (1.9 kg; IQR 1.1-2.9 kg). Liver fat accumulation was similar (P = 0.28) in carriers (33%, IQR 14-79%) and non-carriers (33%, IQR -11 to +87%) of the PNPLA3-I148M variant. Baseline visceral and liver fat content, as well as levels of the liver enzyme gamma-glutamyl transferase (GT), were the strongest positive predictors of liver fat accumulation-in contrast, adiponectin and the fatty acid 17:0 in adipose tissue were the only negative predictors in univariate analyses. A regression model based on eight clinical and metabolic variables could explain 81% of the variation in liver fat accumulation.

Conclusion: Our results suggest there exists a highly inter-individual variation in the accumulation of liver fat in metabolically healthy men and women, in response to an increased energy intake from SFA and carbohydrates that occurs over circa 2 months. This marked variability in liver fat accumulation could largely be predicted by a set of clinical (e.g., GT and BMI) and metabolic (e.g., fatty acids, HOMA-IR, and adiponectin) variables assessed at baseline.