Examining patterns of multivariate, longitudinal symptom experiences among older adults with type 2 diabetes and cancer via cluster analysis

Multimorbidity clusters in adults 50 years or older with and without a history of cancer: National Health Interview Survey, 2018

BACKGROUND

Multimorbidity is increasing among adults in the United States. Yet limited research has examined multimorbidity clusters in persons aged 50 years and older with and without a history of cancer. An increased understanding of multimorbidity clusters may improve the cancer survivorship experience for survivors with multimorbidity.

METHODS

We identified 7580 adults aged 50 years and older with 2 or more diseases-including 811 adults with a history of primary breast, colorectal, cervical, prostate, or lung cancer-from the 2018 National Health Interview Survey. Exploratory factor analysis identified clusters of multimorbidity among cancer survivors and individuals without a history of cancer (controls). Frequency tables and chi-square tests were performed to determine overall differences in sociodemographic characteristics, health-related characteristics, and multimorbidity between groups.

RESULTS

Cancer survivors reported a higher prevalence of having 4 or more diseases compared to controls (57% and 38%, respectively). Our analysis identified 6 clusters for cancer survivors and 4 clusters for controls. Three clusters (pulmonary, cardiac, and liver) included the same diseases for cancer survivors and controls.

CONCLUSIONS

Diseases clustered differently across adults ≥ 50 years of age with and without a history of cancer. Findings from this study may be used to inform clinical care, increase the development and dissemination of multilevel public health interventions, escalate system improvements, and initiate innovative policy reform.

Symptom clusters in breast cancer survivors with and without type 2 diabetes over the cancer trajectory

Objective

This study aimed to investigate symptoms and symptom clusters in breast cancer survivors (BCS) with and without type 2 diabetes across three crucial periods during the cancer trajectory (0-6 months, 12-18 months, and 24-30 months) post-initial chemotherapy.

Methods

Eight common symptoms in both BCS and individuals with diabetes were identified through natural language processing of electronic health records from January 2007 to December 2018. Exploratory factor analysis was employed to discern symptom clusters, evaluating their stability, consistency, and clinical relevance.

Results

Among the 4601 BCS in the study, 20% (n = 905) had a diabetes diagnosis. Gastrointestinal symptoms and fatigue were prevalent in both groups. While BCS in both groups exhibited an equal number of clusters, the composition of these clusters differed. Symptom clusters varied over time between BCS with and without diabetes. BCS with diabetes demonstrated less stability (repeated clusters) and consistency (same individual symptoms comprising clusters) than their counterparts without diabetes. This suggests that BCS with diabetes may experience distinct symptom clusters at pivotal points in the cancer treatment trajectory.

Conclusions

Healthcare providers must be attentive to BCS with diabetes throughout the cancer trajectory, considering intensified and/or unique profiles of symptoms and symptom clusters. Interdisciplinary cancer survivorship models are essential for effective diabetes management in BCS. Implementing a comprehensive diabetes management program throughout the cancer trajectory could alleviate symptoms and symptom clusters, ultimately enhancing health outcomes and potentially reducing healthcare resource utilization.

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancer

Breast cancer is the second most prevalent malignancy affecting women. Postmenopausal women breast tumor is one of the top causes of death in women, accounting for 23% of cancer cases. Type 2 diabetes, a worldwide pandemic, has been connected to a heightened risk of several malignancies, although its association with breast cancer is still uncertain. In comparison to non-diabetic women, women with T2DM had a 23% elevated likelihood of developing breast cancer. It is difficult to determine causative or genetic susceptibility that connect T2DM and breast cancer. We created a large-scale network-based quantitative approach employing unbiased methods to discover abnormally amplified genes in both T2DM and breast cancer, to solve these issues. We performed transcriptome analysis to uncover identical genetic biomarkers and pathways to clarify the connection between T2DM and breast cancer patients. In this study, two RNA-seq datasets (GSE103001 and GSE86468) from the Gene Expression Omnibus (GEO) are used to identify mutually differentially expressed genes (DEGs) for breast cancer and T2DM, as well as common pathways and prospective medicines. Firstly, 45 shared genes (30 upregulated and 15 downregulated) between T2D and breast cancer were detected. We employed gene ontology and pathway enrichment to characterize prevalent DEGs' molecular processes and signal transduction pathways and observed that T2DM has certain connections to the progression of breast cancer. Using several computational and statistical approaches, we created a protein-protein interactions (PPI) network and revealed hub genes. These hub genes can be potential biomarkers, which may also lead to new therapeutic strategies for investigated diseases. We conducted TF-gene interactions, gene-microRNA interactions, protein-drug interactions, and gene-disease associations to find potential connections between T2DM and breast cancer pathologies. We assume that the potential drugs that emerged from this study could be useful therapeutic values. Researchers, doctors, biotechnologists, and many others may benefit from this research.

A qualitative study of blood glucose and side effect self-management among patients with type 2 diabetes undergoing chemotherapy for cancer

Objective

This study aimed to identify the process by which patients with type 2 diabetes who are undergoing chemotherapy for cancer personally manage their blood glucose levels and side effects.

Methods

Semi-structured interviews were conducted with 16 patients with cancer who had completed chemotherapy while taking hypoglycemic drugs. The interview data were analyzed using the modified grounded theory approach proposed by Kinoshita.

Results

Self-management comprised balancing the management of blood glucose levels and side effects according to physical condition. After commencing chemotherapy, participants experienced confusion regarding the side effects and hyperglycemia they have not previously experienced, started struggling with side effects while paying attention to blood glucose fluctuations, experienced simplification of convalescence based on the diabetes experience, and used trial and error to cope with side effects. When participants learned to understand the changes in blood glucose fluctuations and the pattern of physical recovery, they completed chemotherapy by adjusting their physical condition to the treatment by varying self-control.

Conclusions

Healthcare providers need to assist patients receiving chemotherapy to promote self-management of both blood glucose levels and side effects of the chemotherapy, depending on their physical condition. It is essential that patients with type 2 diabetes who are undergoing chemotherapy achieve the ability to self-monitor their blood glucose levels and side effects.

The association between cancer-related fatigue and diabetes from pre-chemotherapy to 6 months post-chemotherapy

PURPOSE

To quantify the relationship between diabetes and fatigue from pre-chemotherapy to 6 months post-chemotherapy for women with breast cancer compared to women without a history of cancer (controls).

METHODS

This was a secondary analysis from a nationwide prospective longitudinal study of female patients with breast cancer undergoing chemotherapy and controls. Diabetes diagnosis (yes/no) was obtained at baseline, and cancer-related fatigue was measured using the Multidimensional Fatigue Symptom Inventory (MFSI) pre-, post-, and 6 months post-chemotherapy in patients; controls were assessed at equivalent time points. Repeated measures mixed effects models estimated the association between fatigue and diabetes controlling for cancer (yes/no), body mass index, exercise and smoking habits, baseline anxiety and depressive symptoms, menopausal status, marital status, race, and education.

RESULTS

Among 439 patients and 235 controls (52.8 ± 10.5 years old), diabetes was twice as prevalent among patients as controls (11.6% vs. 6.8%). At baseline, diabetes was associated with worse fatigue (4.1 ± 1.7 points, p = 0.017). Also, diabetes was associated with clinically meaningful worse fatigue throughout the study period among all participants (5.2 ± 1.9 points, p = 0.008) and patients alone (4.5 ± 2.0, p = 0.023). For the MFSI subdomains among patients, diabetes was associated with worse general (p = 0.005) and mental fatigue (p = 0.026).

CONCLUSIONS

Diabetes was twice as prevalent in women with breast cancer compared to controls, and diabetes was associated with more severe cancer-related fatigue in patients before and after chemotherapy and at 6 months post-chemotherapy. Interventions that address diabetes management may also help address cancer-related fatigue during chemotherapy treatment.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01382082, first posted June 27, 2011.

Glycemic Excursion, Adverse Drug Reactions, and Self-Management in Diabetes Patients Undergoing Chemotherapy: A Literature Review

The purpose of this study was to identify the state of self-management in patients with diabetes who underwent chemotherapy, by referring to fluctuations in glycemic excursion and adverse drug reaction. We conducted a literature search in May 2021 using PubMed, CINAHL, and Ichushi-Web databases with “Cancer AND Diabetes AND Chemotherapy” as keywords. Based on our criteria, 25 articles were selected, and a review matrix sheet was created for the analysis of fluctuations in glycemic excursion and any adverse drug reaction to diabetes in patients undergoing chemotherapy. Substantial increases and unpredictable fluctuations in glycemic excursion were observed in these patients. In addition, an increase or change in the treatment dose was prevalent. Primarily, peripheral neuropathy and infection were reported as common adverse drug reactions. The risk of adverse drug reactions was especially high for patients with diabetes undergoing chemotherapy; furthermore, among this cohort, the detrimental effects were more likely to exacerbate into a severe condition that required special attention. Almost inevitably, the implementation rate of diabetes self-management programs decreased on the 8^th week after the commencement of chemotherapy. Considering the findings of large individual differences in fluctuation in this cohort, tailored assistance that is appropriate for each patient's chemotherapy regimen or blood glucose level is of paramount importance. Support of patient self-management to achieve stable blood glucose levels and thus prevent adverse drug reactions was a key component in the successful completion of chemotherapy and improved patient outcomes for this group of special needs patients.

Importance of Glycemic Control in Cancer Patients with Diabetes: Treatment through End of Life

Cancer patients with diabetes are at increased risk for developing infections, being hospitalized, and requiring chemotherapy reductions or stoppages. While it has been hypothesized that glycemic control increases the risk for these adverse events, few studies have explored this hypothesis. The purpose of this paper is to discuss the importance of glycemic control in patients with diabetes and cancer during treatment through end of life. Glycemic control was found to play a role; the overall level of health-related quality of life experienced by patients with cancer and diabetes, level of symptom severity experienced and can impact the overall survival of the individual. Evidence-based policies and practice guidelines also need to be developed to help clinicians manage these patients during all phases of care. Using diabetes educators and advance practice, nurses to provide management and care coordination services need to be considered. Survivorship care plans should address both cancer and diabetes management. Finally, glycemic control should continue through end of life, with the main goal of avoiding hypoglycemic events.