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Pregled bibliografske jedinice broj: 1163932

Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity


Zvonimir Bosnic, Pinar Yildirim, František Babič, Ines Šahinović, Thomas Wittlinger, Ivo Martinović and Ljiljana Trtica Majnaric
Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity // Healthcare, 9 (2021), 1687; 1, 24 doi:10.3390/ healthcare9121687 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1163932 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity
(Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing)

Autori
Zvonimir Bosnic, Pinar Yildirim, František Babič, Ines Šahinović, Thomas Wittlinger, Ivo Martinović and Ljiljana Trtica Majnaric

Izvornik
Healthcare (2227-9032) 9 (2021), 1687; 1, 24

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
diabetes type 2 ; chronic inflammation ; complex chronic diseases ; primary care patients ; phenotyping ; data mining ; clustering techniques

Sažetak
Diabetes mellitus type 2 (DM2) is a complex disease associated with chronic inflammation, end‐ organ damage, and multiple comorbiditinaes. Initiatives are emerging for a more persol‐ized approach in managing DM2 patients. We hypothesized that by clustering inflammatory markers with variables indicating the sociodemographic and clinical contexts of patients with DM2, we could gain insights into the hidden phenotypes and the underlying pathophysiological back‐ grounds thereof. We applied the k‐means algorithm and a total of 30 variables in a group of 174 primary care (PC) patients with DM2 aged 50 years and above and of both genders. We included some emerging markers of inflammation, specifically, neutrophil‐to‐lymphocyte ratio (NLR) and the cytokines IL‐17A and IL‐37. Multiple regression models were used to assess associations of inflammatory markers with other variables. Overall, we observed that the cytokines were more variable than the marker NLR. The set of inflammatory markers was needed to indicate the capacity of patients in the clusters for inflammatory cell recruitment from the circulation to the tissues, and subsequently for the progression of end‐organ damage and vascular complications. The hypothalamus–pituitary–thyroid hormonal axis, in addition to the cytokine IL‐37, may have a suppressive, inflammation‐regulatory role. These results can help PC physicians with their clinical reasoning by reducing the complexity of diabetic patients.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti



POVEZANOST RADA


Ustanove:
Medicinski fakultet, Osijek

Profili:

Avatar Url Zvonimir Bosnić (autor)

Avatar Url Ljiljana Majnarić (autor)

Poveznice na cjeloviti tekst rada:

doi

Citiraj ovu publikaciju:

Zvonimir Bosnic, Pinar Yildirim, František Babič, Ines Šahinović, Thomas Wittlinger, Ivo Martinović and Ljiljana Trtica Majnaric
Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity // Healthcare, 9 (2021), 1687; 1, 24 doi:10.3390/ healthcare9121687 (međunarodna recenzija, članak, znanstveni)
Zvonimir Bosnic, Pinar Yildirim, František Babič, Ines Šahinović, Thomas Wittlinger, Ivo Martinović and Ljiljana Trtica Majnaric (2021) Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity. Healthcare, 9 (1687), 1, 24 doi:10.3390/ healthcare9121687.
@article{article, year = {2021}, pages = {24}, DOI = {10.3390/ healthcare9121687}, chapter = {1}, keywords = {diabetes type 2, chronic inflammation, complex chronic diseases, primary care patients, phenotyping, data mining, clustering techniques}, journal = {Healthcare}, doi = {10.3390/ healthcare9121687}, volume = {9}, number = {1687}, issn = {2227-9032}, title = {Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing Patients’ Complexity}, keyword = {diabetes type 2, chronic inflammation, complex chronic diseases, primary care patients, phenotyping, data mining, clustering techniques}, chapternumber = {1} }
@article{article, year = {2021}, pages = {24}, DOI = {10.3390/ healthcare9121687}, chapter = {1}, keywords = {diabetes type 2, chronic inflammation, complex chronic diseases, primary care patients, phenotyping, data mining, clustering techniques}, journal = {Healthcare}, doi = {10.3390/ healthcare9121687}, volume = {9}, number = {1687}, issn = {2227-9032}, title = {Clustering Inflammatory Markers with Sociodemographic and Clinical Characteristics of Patients with Diabetes Type 2 Can Support Family Physicians’ Clinical Reasoning by Reducing}, keyword = {diabetes type 2, chronic inflammation, complex chronic diseases, primary care patients, phenotyping, data mining, clustering techniques}, chapternumber = {1} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • Social Science Citation Index (SSCI)
    • SCI-EXP, SSCI i/ili A&HCI


Citati:





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