Glycaemic control in people with type 2 diabetes mellitus during and after cancer treatment: A systematic review and meta-analysis

Background Cancer and Diabetes Mellitus (DM) are leading causes of death worldwide and the prevalence of both is escalating. People with co-morbid cancer and DM have increased morbidity and premature mortality compared with cancer patients with no DM. The reasons for this are likely to be multifaceted but will include the impact of hypo/hyperglycaemia and diabetes therapies on cancer treatment and disease progression. A useful step toward addressing this disparity in treatment outcomes is to establish the impact of cancer treatment on diabetes control. Aim The aim of this review is to identify and analyse current evidence reporting glycaemic control (HbA1c) during and after cancer treatment. Methods Systematic searches of published quantitative research relating to comorbid cancer and type 2 diabetes mellitus were conducted using databases, including Medline, Embase, PsychINFO, CINAHL and Web of Science (February 2017). Full text publications were eligible for inclusion if they: were quantitative, published in English language, investigated the effects of cancer treatment on glycaemic control, reported HbA1c (%/mmols/mol) and included adult populations with diabetes. Means, standard deviations and sample sizes were extracted from each paper; missing standard deviations were imputed. The completed datasets were analysed using a random effects model. A mixed-effects analysis was undertaken to calculate mean HbA1c (%/mmols/mol) change over three time periods compared to baseline. Results The available literature exploring glycaemic control post-diagnosis was mixed. There was increased risk of poor glycaemic control during this time if studies of surgical treatment for gastric cancer are excluded, with significant differences between baseline and 12 months (p < 0.001) and baseline and 24 months (p = 0.002). Conclusion We found some evidence to support the contention that glycaemic control during and/or after non-surgical cancer treatment is worsened, and the reasons are not well defined in individual studies. Future studies should consider the reasons why this is the case

Socioeconomic disparities in breast cancer survival: relation to stage at diagnosis, treatment and race

<p>Abstract</p> <p>Background</p> <p>Previous studies have documented lower breast cancer survival among women with lower socioeconomic status (SES) in the United States. In this study, I examined the extent to which socioeconomic disparity in breast cancer survival was explained by stage at diagnosis, treatment, race and rural/urban residence using the Surveillance, Epidemiology, and End Results (SEER) data.</p> <p>Methods</p> <p>Women diagnosed with breast cancer during 1998-2002 in the 13 SEER cancer registry areas were followed-up to the end of 2005. The association between an area-based measure of SES and cause-specific five-year survival was estimated using Cox regression models. Six models were used to assess the extent to which SES differences in survival were explained by clinical and demographical factors. The base model estimated the hazard ratio (HR) by SES only and then additional adjustments were made sequentially for: 1) age and year of diagnosis; 2) stage at diagnosis; 3) first course treatment; 4) race; and 5) rural/urban residence.</p> <p>Results</p> <p>An inverse association was found between SES and risk of dying from breast cancer (p < 0.0001). As area-level SES falls, HR rises (1.00 → 1.05 → 1.23 → 1.31) with the two lowest SES groups having statistically higher HRs. This SES differential completely disappeared after full adjustment for clinical and demographical factors (p = 0.20).</p> <p>Conclusion</p> <p>Stage at diagnosis, first course treatment and race explained most of the socioeconomic disparity in breast cancer survival. Targeted interventions to increase breast cancer screening and treatment coverage in patients with lower SES could reduce much of socioeconomic disparity.</p

Pre-existing diabetes and breast cancer prognosis among elderly women.

BACKGROUND: The objective of this study was to assess the impact of pre-existing diabetes on breast cancer prognosis. METHODS: Women (n=2833) with centrally confirmed invasive breast cancer in the Women's Health Initiative, who were linked to Medicare claims data (CMS) were followed from the date of breast cancer diagnosis to date of death or 20 September 2013. Information on diabetes was identified through the CMS Chronic Condition Warehouse algorithm. Cox proportional hazard regression was used to estimate adjusted hazard ratios for overall mortality. A competing risks model (proportional subdistribution) model was used to estimate hazard ratios for breast cancer-specific mortality. RESULTS: Women with diabetes were more likely to have factors related to delayed diagnosis (less recent mammograms, and more advanced cancer stage) and were less likely to receive radiation therapy. Compared with women without diabetes, women with diabetes had significantly increased risk of overall mortality (HR=1.57, 95% CI: 1.23–2.01) and had nonsignificantly increased risk for breast cancer-specific mortality (HR=1.36, 95% CI: 0.86–2.15) before adjustment for factors related to delayed diagnosis and treatment. Adjustment for these factors resulted in a little change in the association of diabetes with overall mortality risk, but further attenuated the point estimate for breast cancer-specific mortality. CONCLUSIONS: Our study provides additional evidence that pre-existing diabetes increases the risk of total mortality among women with breast cancer. Very large studies with data on breast cancer risk factors, screening and diagnostic delays, treatment choices, and the biological influence of diabetes on breast cancer will be needed to determine whether diabetes also increases the risk for breast cancer-specific mortality