Opposite trends in incidence of breast cancer in young and old female cohorts in Hungary and the impact of the Covid-19 pandemic: a nationwide study between 2011-2020.

Background: This nationwide study examined breast cancer (BC) incidence and mortality rates in Hungary between 2011-2019, and the impact of the Covid-19 pandemic on the incidence and mortality rates in 2020 using the databases of the National Health Insurance Fund (NHIF) and Central Statistical Office (CSO) of Hungary. Methods: Our nationwide, retrospective study included patients who were newly diagnosed with breast cancer (International Codes of Diseases ICD)-10 C50) between Jan 1, 2011 and Dec 31, 2020. Age-standardized incidence and mortality rates (ASRs) were calculated using European Standard Populations (ESP). Results: 7,729 to 8,233 new breast cancer cases were recorded in the NHIF database annually, and 3,550 to 4,909 all-cause deaths occurred within BC population per year during 2011-2019 period, while 2,096 to 2,223 breast cancer cause-specific death was recorded (CSO). Age-standardized incidence rates varied between 116.73 and 106.16/100,000 PYs, showing a mean annual change of -0.7% (95% CI: -1.21%-0.16%) and a total change of -5.41% (95% CI: -9.24 to -1.32). Age-standardized mortality rates varied between 26.65-24.97/100,000 PYs (mean annual change: -0.58%; 95% CI: -1.31-0.27%; p=0.101; total change: -5.98%; 95% CI: -13.36-2.66). Age-specific incidence rates significantly decreased between 2011 and 2019 in women aged 50-59, 60-69, 80-89, and ≥90 years (-8.22%, -14.28%, -9.14%, and -36.22%, respectively), while it increased in young females by 30.02% (95%CI 17,01%- 51,97%) during the same period. From 2019 to 2020 (in first COVID-19 pandemic year), breast cancer incidence nominally decreased by 12% (incidence rate ratio [RR]: 0.88; 95% CI: 0.69-1.13; 2020 vs. 2019), all-cause mortality nominally increased by 6% (RR: 1.06; 95% CI: 0.79-1.43) among breast cancer patients, and cause-specific mortality did not change (RR: 1.00; 95%CI: 0.86-1.15). Conclusion: The incidence of breast cancer significantly decreased in older age groups (≥50 years), oppositely increased among young females between 2011 and 2019, while cause-specific mortality in breast cancer patients showed a non-significant decrease. In 2020, the Covid-19 pandemic resulted in a nominal, but not statistically significant, 12% decrease in breast cancer incidence, with no significant increase in cause-specific breast cancer mortality observed during 2020.

Interrelationship of hemoglobin A1c level lipid profile, uric acid, C-reactive protein levels and age in a large hospital database.

INTRODUCTION: Hemoglobin A1c (HbA1c) is used to monitor glucose homeostasis and to identify risk for diabetes. As diabetic patients are frequently present with dyslipidaemia, low-grade inflammation and hyperuricemia, we tested whether HbA1c levels can be estimated having the information about lipid profile, uric acid (UA) and C-reactive protein (CRP) levels. We developed formulas to describe the association of these parameters with HbA1c levels. METHODS: Data of 9599 male and 10,817 female patients, measured between 2008 and 2018, were analysed. Patients represented a general hospital patient population with overrepresentation of those with elevated HbA1c over 5.6%. The impact of gender, age, CRP, lipid profile and UA levels on HbA1c % on HbA1c levels was tested with multiple linear regression model. The magnitude of effects of individual factors was used to develop formulas to describe the association between HbA1c and other cardiometabolic parameters. With these formulas we estimated median HbA1c values in each age in both gender and compared them to measured HbA1c levels. RESULTS: The developed formulas are as follow: HbA1c (estimated) in women = 0.752 + 0.237*log10(HDL/cholesterol) + 0.156*log10 (cholesterol) + 0.077*log10 (triglyceride) + 0.025*log10(CRP) +0.001*log10 (age) -0.026*log10(HDL/LDL) -0.063*log10 (uric acid)-0.075*log10 (LDL)-0.199*log10(HDL); HbA1c (estimated) in men = 1.146 + 0.08*log10 (triglyceride) + 0.046*log10(CRP) + 0.01*log10 (cholesterol) + 0.001*log10 (age) -0.014*log10(HDL)-0.018*log10(HDL/LDL)-0.025*log10(HDL/cholesterol) -0.068*log10 (LDL)-0.159*log10 (uric acid) Between 20 and 70 years of age, estimated HbA1c matched perfectly to measured HbA1c in. CONCLUSION: At population level, HbA1c levels can be estimated almost exactly based on lipid profile, CRP and uric acid levels in female patients between 20 and 70 years.

Improved characterization of EV preparations based on protein to lipid ratio and lipid properties.

In recent years the study of extracellular vesicles has gathered much scientific and clinical interest. As the field is expanding, it is becoming clear that better methods for characterization and quantification of extracellular vesicles as well as better standards to compare studies are warranted. The goal of the present work was to find improved parameters to characterize extracellular vesicle preparations. Here we introduce a simple 96 well plate-based total lipid assay for determination of lipid content and protein to lipid ratios of extracellular vesicle preparations from various myeloid and lymphoid cell lines as well as blood plasma. These preparations included apoptotic bodies, microvesicles/microparticles, and exosomes isolated by size-based fractionation. We also investigated lipid bilayer order of extracellular vesicle subpopulations using Di-4-ANEPPDHQ lipid probe, and lipid composition using affinity reagents to clustered cholesterol (monoclonal anti-cholesterol antibody) and ganglioside GM1 (cholera toxin subunit B). We have consistently found different protein to lipid ratios characteristic for the investigated extracellular vesicle subpopulations which were substantially altered in the case of vesicular damage or protein contamination. Spectral ratiometric imaging and flow cytometric analysis also revealed marked differences between the various vesicle populations in their lipid order and their clustered membrane cholesterol and GM1 content. Our study introduces for the first time a simple and readily available lipid assay to complement the widely used protein assays in order to better characterize extracellular vesicle preparations. Besides differentiating extracellular vesicle subpopulations, the novel parameters introduced in this work (protein to lipid ratio, lipid bilayer order, and lipid composition), may prove useful for quality control of extracellular vesicle related basic and clinical studies.