AimTo explore how family caregivers experience involvement in palliative care.DesignA qualitative design with a narrative approach was used.MethodsPurposive sampling and narrative interviews were conducted. Eleven bereaved family caregivers for patients with cancer receiving palliative care were interviewed in Mid‐Norway between November 2016–May 2017.ResultsWe identified four themes related to family caregivers' experiences of involvement in the early, middle, terminal and bereavement phases of palliative care: (a) limited involvement in the early phase; (b) emphasis on patient‐centred care in the middle phase; (c) lack of preparation for the dying phase; and (d) lack of systematic follow‐up after death. Family caregivers experienced low level of involvement throughout the palliative pathway.ConclusionThe involvement of family caregivers in palliative care may not be proportional to their responsibilities. The needs of family caregivers should be addressed in nursing education to give nurses competence to support family caregivers in providing home‐based care.

Pregnancy implies delicate immunological balance between two individuals, with constant changes and adaptions in response to maternal capacity and fetal demands. We performed cytokine profiling of 1149 longitudinal serum samples from 707 pregnant women to map immunological changes from first trimester to term and beyond. The serum levels of 22 cytokines and C-reactive protein (CRP) followed diverse but characteristic trajectories throughout pregnancy, consistent with staged immunological adaptions. Eotaxin showed a particularly robust decrease throughout pregnancy. A strong surge in cytokine levels developed when pregnancies progressed beyond term and the increase was amplified as labor approached. Maternal obesity, smoking and pregnancies with large fetuses showed sustained increase in distinct cytokines throughout pregnancy. Multiparous women had increased cytokine levels in the first trimester compared to nulliparous women with higher cytokine levels in the third trimester. Fetal sex affected first trimester cytokine levels with increased levels in pregnancies with a female fetus. These findings unravel important immunological dynamics of pregnancy, demonstrate how both maternal and fetal factors influence maternal systemic cytokines, and serve as a comprehensive reference for cytokine profiles in normal pregnancies.

Longitudinal intervention studies with repeated measurements over time are an important type of experimental design in biomedical research. Due to the advent of “omics”-sciences (genomics, transcriptomics, proteomics, metabolomics), longitudinal studies generate increasingly multivariate outcome data. Analysis of such data must take both the longitudinal intervention structure and multivariate nature of the data into account. The ASCA+-framework combines general linear models with principal component analysis and can be used to separate and visualize the multivariate effect of different experimental factors. However, this methodology has not yet been developed for the more complex designs often found in longitudinal intervention studies, which may be unbalanced, involve randomized interventions, and have substantial missing data. Here we describe a new methodology, repeated measures ASCA+ (RM-ASCA+), and show how it can be used to model metabolic changes over time, and compare metabolic changes between groups, in both randomized and non-randomized intervention studies. Tools for both visualization and model validation are discussed. This approach can facilitate easier interpretation of data from longitudinal clinical trials with multivariate outcomes.

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

BACKGROUND: Breast cancer treatment has metabolic side effects, potentially affecting risk of cardiovascular disease (CVD) and recurrence. We aimed to compare alterations in serum metabolites and lipoproteins during treatment between recipients and nonrecipients of chemotherapy, and describe metabolite profiles associated with treatment-related weight gain. METHODS: This pilot study includes 60 stage I/II breast cancer patients who underwent surgery and were treated according to national guidelines. Serum sampled pre-surgery and after 6 and 12 months was analysed by MR spectroscopy and mass spectrometry. In all, 170 metabolites and 105 lipoprotein subfractions were quantified. RESULTS: The metabolite and lipoprotein profiles of chemotherapy recipients and non-recipients changed significantly 6 months after surgery (p < 0.001). Kynurenine, the lipid signal at 1.55-1.60 ppm, ADMA, 2 phosphatidylcholines (PC aa C38:3, PC ae C42:1), alpha-aminoadipic acid, hexoses and sphingolipids were increased in chemotherapy recipients after 6 months. VLDL and small dense LDL increased after 6 months, while HDL decreased, with triglyceride enrichment in HDL and LDL. At baseline, weight gainers had less acylcarnitines, phosphatidylcholines, lyso-phosphatidylcholines and sphingolipids, and showed an inflammatory lipid profile. CONCLUSION: Chemotherapy recipients exhibit metabolic changes associated with inflammation, altered immune response and increased risk of CVD. Altered lipid metabolism may predispose for treatment-related weight gain.

Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial leastsquares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.

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The multimodal treatment of breast cancer may induce long term effects on the metabolic profile and increase the risk of future cardiovascular disease. In this study, we characterized longitudinal changes in serum lipoprotein subfractions and metabolites after breast cancer treatment, aiming to determine the long-term effect of different treatment modalities. Further, we investigated the prognostic value of treatment-induced changes in breast cancer-specific and overall 10-year survival. In this study, serum samples from breast cancer patients (n = 250) were collected repeatedly before and after radiotherapy, and serum metabolites and lipoprotein subfractions were quantified by NMR spectroscopy. Longitudinal changes were assessed by univariate and multivariate data analysis methods applicable for repeated measures. Distinct changes were detectable in levels of lipoprotein subfractions and circulating metabolites during the first year, with similar changes despite large differences in treatment regimens. We detect increased free cholesterol and decreased esterified cholesterol levels of HDL subfractions, a switch towards larger LDL particles and higher total LDL-cholesterol, in addition to a switch in the glutamine-glutamate ratio. Non-survivors had different lipid profiles from survivors already at baseline. To conclude, our results show development towards an atherogenic lipid profile in breast cancer patients with different treatment regimens.