| 1. |
- Cossarizza, A., et al.
(författare)
-
Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
- 2019
-
Ingår i: European Journal of Immunology. - : Wiley. - 0014-2980 .- 1521-4141. ; 49:10, s. 1457-1973
-
Tidskriftsartikel (refereegranskat)abstract
- These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
- Moore, Amy, et al.
(författare)
-
Genetically Determined Height and Risk of Non-hodgkin Lymphoma
- 2020
-
Ingår i: Frontiers in Oncology. - : FRONTIERS MEDIA SA. - 2234-943X. ; 9
-
Tidskriftsartikel (refereegranskat)abstract
- Although the evidence is not consistent, epidemiologic studies have suggested that taller adult height may be associated with an increased risk of some non-Hodgkin lymphoma (NHL) subtypes. Height is largely determined by genetic factors, but how these genetic factors may contribute to NHL risk is unknown. We investigated the relationship between genetic determinants of height and NHL risk using data from eight genome-wide association studies (GWAS) comprising 10,629 NHL cases, including 3,857 diffuse large B-cell lymphoma (DLBCL), 2,847 follicular lymphoma (FL), 3,100 chronic lymphocytic leukemia (CLL), and 825 marginal zone lymphoma (MZL) cases, and 9,505 controls of European ancestry. We evaluated genetically predicted height by constructing polygenic risk scores using 833 height-associated SNPs. We used logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI) for association between genetically determined height and the risk of four NHL subtypes in each GWAS and then used fixed-effect meta-analysis to combine subtype results across studies. We found suggestive evidence between taller genetically determined height and increased CLL risk (OR = 1.08, 95% CI = 1.00-1.17, p = 0.049), which was slightly stronger among women (OR = 1.15, 95% CI: 1.01-1.31, p = 0.036). No significant associations were observed with DLBCL, FL, or MZL. Our findings suggest that there may be some shared genetic factors between CLL and height, but other endogenous or environmental factors may underlie reported epidemiologic height associations with other subtypes.
|
|
| 9. |
- Fornaro, M., et al.
(författare)
-
MULTIMORBIDITY AND PROMIS HEALTH OUTCOMES IN PATIENTS WITH IDIOPATHIC INFLAMMATORY MYOPATHIES : DATA FROM A LARGE, GLOBAL E-SURVEY (COVAD STUDY)
- 2023
-
Ingår i: Annals of the Rheumatic Diseases. - : HighWire Press. - 0003-4967 .- 1468-2060. ; 82:Suppl. 1, s. 942-943
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Prevalence of comorbidities and their impact on health outcomes in Idiopathic inflammatory myopathies (IIMs) is limited.Objectives: This study aimed to explore the prevalence of multimorbidity in patients with IIMs, other autoimmune rheumatic diseases (AIRDs) and Healthy controls (HCs). We further explore the impact of comorbidities on patients’ physical, mental, and social health assessed by the Patient-Reported Outcome Measurement Information System (PROMIS instruments).Methods: Data for this study were acquired from the COVAD 2 e-survey hosted by a study group consisting of 167 collaborators in 110 countries. Basic multimorbidity (BM) was defined as the co-occurrence of two or more comorbidities in an individual, while complex multimorbidity (CM) signified the co-occurrence of 3 or more chronic conditions affecting 3 or more different organ systems. PROMIS global physical health (PGP), mental health (PGM), fatigue 4a (F4a) and physical function short form (SF10) were analysed using descriptive statistics and linear regression models. Hierarchical Clustering on Principal Components was performed to outline the grouping.Results: Of 10740 complete respondents, 1558 IIMs, 4591 AIRDs and 3652 HCs were analysed. Individuals with IIMs exhibited high burden of any comorbidity (OR: 1.62 vs AIRDs and 2.95 vs HCs,p<0.01), BM (OR 1.66 vs AIRDs and 3.52 vs HCs,p<0.01), CM (OR: 1.69 vs AIRDs and 6.23 vs HCs,p<0.01), and mental health disorders (MHDs) (OR 1.33 vs AIRDs and 2.63 vs HCs,p<0.01).IIM patients with comorbidities (and MHDs) had worse physical function (low PGP, PGM, SF10 and higher F4a scores, all p<0.001). Worse physical function (PGP) was predicted by age (0.35; 0.030), active disease (-1.51; <0.001), BM (-1.11; <0.001), and MHDs (-1.47; <0.001). PGM was impacted by age (0.51; 0.004), active disease (-1.34, <0.001), BM (-0.75; 0.001) and MHDs (-2.22; <0.001). Determinants of SF10a were age (-3.86; <0.001), active disease (-7.03, <0.001), female (2.85, <0.001), BM (-2.95; <0.001) and MHDs (-2.37; <0.001). Fatigue (F4a) was impacted by age (-0.96, <0.001), active disease (1.45, <0.001), country human development index (0.95; 0.036), BM (1.11; <0.001); and MHDs (2.17; <0.001).Four distinct clusters (Figure 1A, Table 1) were identified i.e., cluster 0: lower burden of comorbidities and good health status; cluster 1: older patients, whit higher burden of comorbidities and poor health status, cluster 2: patients with higher prevalence of MHDs, lower PGP and PGM; and higher F4a scores; and lastly Cluster 3 that comprised older patients with an average burden of comorbidities and overall good health status according to PROMIS scores.Dermatomyositis, anti-synthetase syndrome, necrotizing autoimmune myopathy were similarly represented in all clusters, whilst inclusion body myositis and polymyositis were more predominant in clusters 1 (40.6% and 17.2%) and 3 (32 % and 17.5%), while overlap myositis was more represented in cluster 2 (25.6%) and 0 (32.7%) (Figure 1B).Conclusion: Patients with IIMs have a higher burden of comorbidities that adversely impact physical and mental health, calling for optimized approaches for holistic patient management.
|
|
| 10. |
- Gupta, L., et al.
(författare)
-
COMORBIDITIES, COMPLEX MULTIMORBIDITY AND PROMIS HEALTH OUTCOMES AMONGAUTOIMMUNE RHEUMATIC DISEASES : DATA FROM THE COVAD STUDY
- 2023
-
Ingår i: Annals of the Rheumatic Diseases. - : HighWire Press. - 0003-4967 .- 1468-2060. ; 82:Suppl. 1, s. 555-556
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Comorbidities have a profound impact on the QoL of patients living with autoimmune rheumatic diseases (AIRDs). Unfortunately, global data on the burden of comorbidities and its impact on health outcomes in this vulnerable group is scarce.Objectives: We studied the prevalence, distribution and clustering of comorbidities and multimorbidity among patients with AIRDs and healthy controls (HCs) and its impact on health outcomes, utilizing data from the ongoing 2nd COVAD study.Methods: The COVAD study is a global e-survey that embodies patient voice while empowering collaborators and young researchers. The study group of 157 physicians across 106 countries from February-June 2022 captured details of AIRDs, autoimmune and non-autoimmune comorbidities, and validated patient reported outcomes. Human Development Index (UNDP 2021-22) of country of residence was taken as a surrogate marker for socioeconomic status (SES).Basic multimorbidity (BM), Complex multimorbidity (CM), Autoimmune multimorbidity (AM) are defined as the co-occurrence of ≥2 non-rheumatic comorbidities, ≥3 non-rheumatic chronic conditions affecting ≥3 different organ systems [1] and ≥3 autoimmune diseases (AIDs) in an individual respectively.PROMIS global physical health (PGP), mental health (PGM), fatigue 4a (F4a) and physical function short form (SF10) scores were calculated for the different groups and compared using descriptive statistics, linear regression and cluster analysis (hierarchical followed by K means).Results: Of 17,612 total respondents, 6149 (62.7%) had underlying AIRDs and 3652 (37.3%) were HCs, with female (80.8%) and Caucasian (53.9%) predominance in the former.All types of multimorbidity were more frequent in AIRDs than HCs, including any comorbidity (77.1% versus 25.0%; OR: 2.9; 2.7-3.2), BM (21.0% vs 6.2%; 4.0; 3.4-4.6), and CM (3.1% vs 0.5%; 6.4; 3.9-10.4), and with prevalence increasing with age (p<0.001) (Figure 1A, B). Comorbidity prevalence was the highest among Americans and Australians (72% each).Patients with AIRDs had poorer health outcomes than HCs, including lower PGP, PGM, SF10, F4a scores (all p<0.001). Among AIRDs, those with comorbidities had lower physical function and PROMIS scores (PGP, PGM, and SF10), and reported fatigue more often (all p<0.001).Female gender, and underlying BM and AM particularly predisposed patients to worse physical health (lower PGP, lower SF10a) and mental health outcomes (lower PGM). While advanced age (-1.815; <0.001), and lower SES (0.871; 0.027) specifically predicted poorer physical function (lower SF10a). Fatigue (higher F4a) was seen more frequently among women (1.711; <0.001), and those with BM (1.142; 0.002); AM (1.768; 0.011), and higher SEC (0.478; 0.016).Cluster analysis of patients with AIRDs revealed 2 clusters (Figure 1C 1D); cluster 1 with low PGP, PGM, SF10 and high F4a; cluster 2 with high PGP, PGM, SF10 and low F4a. The clusters differed predominantly based on the frequency of comorbidities; any comorbidity (59.7% vs 41.8%; p<0.001), BM (28.5% vs 14.7%; 0.001); CM (4.5% vs 1.9%; <0.001), and AM (10.0% vs 4.0%; <0.001).Conclusion: Comorbidities complicate three-quarters of individuals living with AIRDs, and have an outsized impact on self-reported physical function, perceived fatigue, and QoL. Substantial regional differences call for further exploration of key drivers of this important aspect to allow optimized multidisciplinary and holistic care in anticipation of poorer outcomes.Reference: [1]Harrison C, Britt H, Miller G, Henderson J. Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice. BMJ Open. 2014 Jul 1;4(7):e004694.
|
|
