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Article ; Online: Associations of Diet with Health Outcomes in the UK Biobank: A Systematic Review.

Navratilova, Hana F / Lanham-New, Susan / Whetton, Anthony D / Geifman, Nophar

2024 Volume 16, Issue 4

Abstract: The UK Biobank is a cohort study that collects data on diet, lifestyle, biomarkers, and health to examine diet-disease associations. Based on the UK Biobank, we reviewed 36 studies on diet and three health conditions: type 2 diabetes (T2DM), ... ...

Abstract	The UK Biobank is a cohort study that collects data on diet, lifestyle, biomarkers, and health to examine diet-disease associations. Based on the UK Biobank, we reviewed 36 studies on diet and three health conditions: type 2 diabetes (T2DM), cardiovascular disease (CVD), and cancer. Most studies used one-time dietary data instead of repeated 24 h recalls, which may lead to measurement errors and bias in estimating diet-disease associations. We also found that most studies focused on single food groups or macronutrients, while few studies adopted a dietary pattern approach. Several studies consistently showed that eating more red and processed meat led to a higher risk of lung and colorectal cancer. The results suggest that high adherence to "healthy" dietary patterns (consuming various food types, with at least three servings/day of whole grain, fruits, and vegetables, and meat and processed meat less than twice a week) slightly lowers the risk of T2DM, CVD, and colorectal cancer. Future research should use multi-omics data and machine learning models to account for the complexity and interactions of dietary components and their effects on disease risk.
MeSH term(s)	Humans ; Diabetes Mellitus, Type 2/epidemiology ; Diabetes Mellitus, Type 2/etiology ; Cohort Studies ; UK Biobank ; Diet ; Fruit ; Colorectal Neoplasms/epidemiology ; Colorectal Neoplasms/etiology ; Colorectal Neoplasms/prevention & control ; Cardiovascular Diseases/epidemiology ; Cardiovascular Diseases/etiology ; Outcome Assessment, Health Care ; Risk Factors
Language	English
Publishing date	2024-02-13
Publishing country	Switzerland
Document type	Systematic Review ; Journal Article ; Review
ZDB-ID	2518386-2
ISSN	2072-6643 ; 2072-6643
ISSN (online)	2072-6643
ISSN	2072-6643
DOI	10.3390/nu16040523
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Identifying developments over a decade in the digital health and telemedicine landscape in the UK using quantitative text mining.

Geifman, Nophar / Armes, Jo / Whetton, Anthony D

Frontiers in digital health

2023 Volume 5, Page(s) 1092008

Abstract: The use of technologies that provide objective, digital data to clinicians, carers, and service users to improve care and outcomes comes under the unifying term Digital Health. This field, which includes the use of high-tech health devices, telemedicine ... ...

Abstract	The use of technologies that provide objective, digital data to clinicians, carers, and service users to improve care and outcomes comes under the unifying term Digital Health. This field, which includes the use of high-tech health devices, telemedicine and health analytics has, in recent years, seen significant growth in the United Kingdom and worldwide. It is clearly acknowledged by multiple stakeholders that digital health innovations are necessary for the future of improved and more economic healthcare service delivery. Here we consider digital health-related research and applications by using an informatics tool to objectively survey the field. We have used a quantitative text-mining technique, applied to published works in the field of digital health, to capture and analyse key approaches taken and the diseases areas where these have been applied. Key areas of research and application are shown to be cardiovascular, stroke, and hypertension; although the range seen is wide. We consider advances in digital health and telemedicine in light of the COVID-19 pandemic.
Language	English
Publishing date	2023-04-17
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-253X
ISSN (online)	2673-253X
DOI	10.3389/fdgth.2023.1092008
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Article: Correction: Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry.

Ramírez Medina, Carlos R / Ali, Ibrahim / Baricevic-Jones, Ivona / Odudu, Aghogho / Saleem, Moin A / Whetton, Anthony D / Kalra, Philip A / Geifman, Nophar

Clinical proteomics

2024 Volume 21, Issue 1, Page(s) 25

Language	English
Publishing date	2024-03-28
Publishing country	England
Document type	Published Erratum
ZDB-ID	2205154-5
ISSN	1542-6416
ISSN	1542-6416
DOI	10.1186/s12014-024-09471-y
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Article ; Online: Identifying developments over a decade in the digital health and telemedicine landscape in the UK using quantitative text mining

Nophar Geifman / Jo Armes / Anthony D. Whetton

Frontiers in Digital Health, Vol

2023 Volume 5

Abstract	The use of technologies that provide objective, digital data to clinicians, carers, and service users to improve care and outcomes comes under the unifying term Digital Health. This field, which includes the use of high-tech health devices, telemedicine and health analytics has, in recent years, seen significant growth in the United Kingdom and worldwide. It is clearly acknowledged by multiple stakeholders that digital health innovations are necessary for the future of improved and more economic healthcare service delivery. Here we consider digital health-related research and applications by using an informatics tool to objectively survey the field. We have used a quantitative text-mining technique, applied to published works in the field of digital health, to capture and analyse key approaches taken and the diseases areas where these have been applied. Key areas of research and application are shown to be cardiovascular, stroke, and hypertension; although the range seen is wide. We consider advances in digital health and telemedicine in light of the COVID-19 pandemic.
Keywords	digital health ; telemedicine ; United Kingdom ; trends ; text mining ; Medicine ; R ; Public aspects of medicine ; RA1-1270 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	028
Language	English
Publishing date	2023-04-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Do traditional BMI categories capture future obesity? A comparison with trajectories of BMI and incidence of cancer.

Watson, Charlotte / Geifman, Dr Nophar

AMIA ... Annual Symposium proceedings. AMIA Symposium

2021 Volume 2020, Page(s) 1287–1294

Abstract: In 2016, 13 specific obesity related cancers were identified by IARC. Here, using baseline WHO BMI categories, latent profile analysis (LPA) and latent class trajectory modelling (LCTM) we evaluated the usefulness of one-off measures when predicting ... ...

Abstract	In 2016, 13 specific obesity related cancers were identified by IARC. Here, using baseline WHO BMI categories, latent profile analysis (LPA) and latent class trajectory modelling (LCTM) we evaluated the usefulness of one-off measures when predicting cancer risk vs life-course changes. Our results in LPA broadly concurred with the three basic WHO BMI categories, with similar stepwise increase in cancer risk observed. In LCTM, we identified 5 specific trajectories in men and women. Compared to the leanest class, a stepwise increase in risk for obesity related cancer was observed for all classes. When latent class membership was compared to baseline BMI, we found that the trajectories were composed of a range of BMI (baseline) categories. All methods reveal a link between obesity and the 13 cancers identified by IARC. However, the additional information included by LCTM indicates that lifetime BMI may highlight additional group of people that are at risk.
MeSH term(s)	Aged ; Body Mass Index ; Female ; Humans ; Incidence ; Latent Class Analysis ; Male ; Middle Aged ; Neoplasms/complications ; Neoplasms/epidemiology ; Obesity/complications ; Obesity/epidemiology ; Risk Factors
Language	English
Publishing date	2021-01-25
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1942-597X
ISSN (online)	1942-597X
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Towards stratified treatment of JIA: machine learning identifies subtypes in response to methotrexate from four UK cohorts.

Shoop-Worrall, Stephanie J W / Lawson-Tovey, Saskia / Wedderburn, Lucy R / Hyrich, Kimme L / Geifman, Nophar

EBioMedicine

2024 Volume 100, Page(s) 104946

Abstract: Background: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To facilitate ... ...

Abstract	Background: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To facilitate stratified treatment of early JIA, novel methods in machine learning were used to i) identify clusters with distinct disease patterns following MTX initiation; ii) predict cluster membership; and iii) compare clusters to existing treatment response measures. Methods: Discovery and verification cohorts included CYP who first initiated MTX before January 2018 in one of four UK multicentre prospective cohorts of JIA within the CLUSTER consortium. JADAS components (active joint count, physician (PGA) and parental (PGE) global assessments, ESR) were recorded at MTX start and over the following year. Clusters of MTX 'response' were uncovered using multivariate group-based trajectory modelling separately in discovery and verification cohorts. Clusters were compared descriptively to ACR Pedi 30/90 scores, and multivariate logistic regression models predicted cluster-group assignment. Findings: The discovery cohorts included 657 CYP and verification cohorts 1241 CYP. Six clusters were identified: Fast improvers (11%), Slow Improvers (16%), Improve-Relapse (7%), Persistent Disease (44%), Persistent PGA (8%) and Persistent PGE (13%), the latter two characterised by improvement in all features except one. Factors associated with clusters included ethnicity, ILAR category, age, PGE, and ESR scores at MTX start, with predictive model area under the curve values of 0.65-0.71. Singular ACR Pedi 30/90 scores at 6 and 12 months could not capture speeds of improvement, relapsing courses or diverging disease patterns. Interpretation: Six distinct patterns following initiation of MTX have been identified using methods in artificial intelligence. These clusters demonstrate the limitations in traditional yes/no treatment response assessment (e.g., ACRPedi30) and can form the basis of a stratified medicine programme in early JIA. Funding: Medical Research Council, Versus Arthritis, Great Ormond Street Hospital Children's Charity, Olivia's Vision, and the National Institute for Health Research.
MeSH term(s)	Child ; Humans ; Adolescent ; Methotrexate/adverse effects ; Arthritis, Juvenile/drug therapy ; Prospective Studies ; Artificial Intelligence ; Antirheumatic Agents/adverse effects ; Machine Learning ; United Kingdom ; Treatment Outcome
Chemical Substances	Methotrexate (YL5FZ2Y5U1) ; Antirheumatic Agents
Language	English
Publishing date	2024-01-08
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2851331-9
ISSN	2352-3964
ISSN (online)	2352-3964
DOI	10.1016/j.ebiom.2023.104946
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Article: Latent class trajectory modelling: impact of changes in model specification.

Watson, Charlotte / Geifman, Nophar / Renehan, Andrew G

American journal of translational research

2022 Volume 14, Issue 10, Page(s) 7593–7606

Abstract: Latent class trajectory models (LCTMs) are often used to identify subgroups of patients that are clinically meaningful in terms of longitudinal exposure and outcome, e.g. drug response patterns. These models are increasingly applied in medicine and ... ...

Abstract	Latent class trajectory models (LCTMs) are often used to identify subgroups of patients that are clinically meaningful in terms of longitudinal exposure and outcome, e.g. drug response patterns. These models are increasingly applied in medicine and epidemiology. However, in many published studies, it is not clear whether the chosen models, where subgroups of patients are identified, represent real heterogeneity in the population, or whether any associations with clinically meaningful characteristics are accidental. In particular, we note an apparent over-reliance on lowest AIC or BIC values. While these are objective measures of goodness of fit, and can help identify the optimal number of subgroups, they are not sufficient on their own to fully evaluate a given trajectory model. Here we demonstrate how longitudinal latent class models can substantially change by making small modifications in model specification, and the impact of this on the relationship to clinical outcomes. We show that the predicted trajectory patterns and outcome probabilities differ when pre-specified cubic versus linear shapes are tested on the same data. However, both could be interpreted to be the "correct" model. We emphasise that LCTMs, like all unsupervised approaches, are hypotheses generating, and should not be directly implemented in clinical practice without significant testing and validation.
Language	English
Publishing date	2022-10-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	2471058-1
ISSN	1943-8141
ISSN	1943-8141
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Article ; Online: A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases.

Geifman, Nophar / Whetton, Anthony D

Journal of translational medicine

2020 Volume 18, Issue 1, Page(s) 297

Abstract: Background: The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively high ... ...

Abstract	Background: The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively high rates of infection and mortality. A third virus, the H5N1, responsible for avian influenza, has caused infection with some clinical similarities to those in COVID-19 infections. Cytokines, small proteins that modulate immune responses, have been directly implicated in some of the severe responses seen in COVID-19 patients, e.g. cytokine storms. Understanding the immune processes related to COVID-19, and other similar infections, could help identify diagnostic markers and therapeutic targets. Methods: Here we examine data of cytokine, immune cell types, and disease associations captured from biomedical literature associated with COVID-19, Coronavirus in general, SARS, and H5N1 influenza, with the objective of identifying potentially useful relationships and areas for future research. Results: Cytokine and cell-type associations captured from Medical Subject Heading (MeSH) terms linked to thousands of PubMed records, has identified differing patterns of associations between the four corpuses of publications (COVID-19, Coronavirus, SARS, or H5N1 influenza). Clustering of cytokine-disease co-occurrences in the context of Coronavirus has identified compelling clusters of co-morbidities and symptoms, some of which already known to be linked to COVID-19. Finally, network analysis identified sub-networks of cytokines and immune cell types associated with different manifestations, co-morbidities and symptoms of Coronavirus, SARS, and H5N1. Conclusion: Systematic review of research in medicine is essential to facilitate evidence-based choices about health interventions. In a fast moving pandemic the approach taken here will identify trends and enable rapid comparison to the literature of related diseases.
MeSH term(s)	Betacoronavirus ; COVID-19 ; Cluster Analysis ; Comorbidity ; Coronavirus Infections/immunology ; Cytokine Release Syndrome/virology ; Cytokines/immunology ; Hematopoietic Stem Cells/cytology ; Humans ; Immune System ; Influenza A Virus, H5N1 Subtype ; Influenza, Human/immunology ; Lung Diseases/immunology ; Pandemics ; Pneumonia, Viral/immunology ; PubMed ; Publications ; SARS-CoV-2 ; Severe Acute Respiratory Syndrome/immunology
Chemical Substances	Cytokines
Keywords	covid19
Language	English
Publishing date	2020-08-03
Publishing country	England
Document type	Journal Article
ISSN	1479-5876
ISSN (online)	1479-5876
DOI	10.1186/s12967-020-02472-z
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A consideration of publication-derived immune-related associations in Coronavirus and related lung damaging diseases

Nophar Geifman / Anthony D. Whetton

Journal of Translational Medicine, Vol 18, Iss 1, Pp 1-

2020 Volume 11

Abstract: Abstract Background The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively ...

Abstract	Abstract Background The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively high rates of infection and mortality. A third virus, the H5N1, responsible for avian influenza, has caused infection with some clinical similarities to those in COVID-19 infections. Cytokines, small proteins that modulate immune responses, have been directly implicated in some of the severe responses seen in COVID-19 patients, e.g. cytokine storms. Understanding the immune processes related to COVID-19, and other similar infections, could help identify diagnostic markers and therapeutic targets. Methods Here we examine data of cytokine, immune cell types, and disease associations captured from biomedical literature associated with COVID-19, Coronavirus in general, SARS, and H5N1 influenza, with the objective of identifying potentially useful relationships and areas for future research. Results Cytokine and cell-type associations captured from Medical Subject Heading (MeSH) terms linked to thousands of PubMed records, has identified differing patterns of associations between the four corpuses of publications (COVID-19, Coronavirus, SARS, or H5N1 influenza). Clustering of cytokine-disease co-occurrences in the context of Coronavirus has identified compelling clusters of co-morbidities and symptoms, some of which already known to be linked to COVID-19. Finally, network analysis identified sub-networks of cytokines and immune cell types associated with different manifestations, co-morbidities and symptoms of Coronavirus, SARS, and H5N1. Conclusion Systematic review of research in medicine is essential to facilitate evidence-based choices about health interventions. In a fast moving pandemic the approach taken here will identify trends and enable rapid comparison to the literature of related diseases.
Keywords	COVID-19 ; Coronavirus ; SARS ; H5N1 influenza ; Cytokines ; Haematopoietic cells ; Medicine ; R ; covid19
Subject code	610
Language	English
Publishing date	2020-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Associations of specific-age and decade recall body mass index trajectories with obesity-related cancer.

Watson, Charlotte / Renehan, Andrew G / Geifman, Nophar

BMC cancer

2021 Volume 21, Issue 1, Page(s) 502

Abstract: Background: Excess body fatness, commonly approximated by a one-off determination of body mass index (BMI), is associated with increased risk of at least 13 cancers. Modelling of longitudinal BMI data may be more informative for incident cancer ... ...

Abstract	Background: Excess body fatness, commonly approximated by a one-off determination of body mass index (BMI), is associated with increased risk of at least 13 cancers. Modelling of longitudinal BMI data may be more informative for incident cancer associations, e.g. using latent class trajectory modelling (LCTM) may offer advantages in capturing changes in patterns with time. Here, we evaluated the variation in cancer risk with LCTMs using specific age recall versus decade recall BMI. Methods: We obtained BMI profiles for participants from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. We developed gender-specific LCTMs using recall data from specific ages 20 and 50 years (72,513 M; 74,837 W); decade data from 30s to 70s (42,113 M; 47,352 W) and a combination of both (74,106 M, 76,245 W). Using an established methodological framework, we tested 1:7 classes for linear, quadratic, cubic and natural spline shapes, and modelled associations for obesity-related cancer (ORC) incidence using LCTM class membership. Results: Different models were selected depending on the data type used. In specific age recall trajectories, only the two heaviest classes were associated with increased risk of ORC. For the decade recall data, the shapes appeared skewed by outliers in the heavier classes but an increase in ORC risk was observed. In the combined models, at older ages the BMI values were more extreme. Conclusions: Specific age recall models supported the existing literature changes in BMI over time are associated with increased ORC risk. Modelling of decade recall data might yield spurious associations.
MeSH term(s)	Adult ; Age Factors ; Aged ; Body Mass Index ; Female ; Humans ; Male ; Middle Aged ; Neoplasms/etiology ; Obesity/complications ; Young Adult
Language	English
Publishing date	2021-05-05
Publishing country	England
Document type	Journal Article
ISSN	1471-2407
ISSN (online)	1471-2407
DOI	10.1186/s12885-021-08226-4
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