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Background: The association between prolong metformin usage and B12 deficiency has been documented. However, the prevalence estimates of metformin-induced vitamin B12 deficiency showed substantial disparity among studies due to varied study definitions of vitamin B12 deficiency. Metformin blocks the calcium dependent absorption of the vitamin B12-Intrinsic Factor complex at the terminal ileum. Lack of intrinsic factor due to the presence of auto-antibodies to parietal cells (IFA) could lead to vitamin B12 deficiency and subsequently cause peripheral neuropathy. We investigated the prevalence of vitamin B12 deficiency using more sensitive, combined markers of vitamin B12 status (4cB12) and the immuno-biochemical mediators of vitamin B12 deficiency. Methods: In this observational study, 200 consecutive consenting metformin-treated T2DM patients, aged 35 and above, attending the diabetic clinic at KATH were recruited. Vitamin B12 deficiency was classified based on the Fedosov age-normalized wellness quotient. Anthropometric measurement was taken as well as blood samples for immunological and biochemical mediators. Peripheral neuropathy was assessed using the Michigan Neuropathy Screening Instrument (MNSI). Statistical analysis was performed using the R Language for Statistical Computing. Results: Using the combined indicator (4cB12), the prevalence of metformin induced vitamin B12 deficiency was 40.5% whilst the prevalence of MNSI-Q and MNSI-PE diabetic neuropathy was 32.5% and 6.5% respectively. Participants with vitamin B12 deficiency had significantly higher levels of IFA, GPA, TNF-α, TC, LDL and albumin compared to those with normal vitamin B12 levels (p < 0.05). Correlation analysis revealed a statistically significant negative association between 4cB12 and the immunological markers [IFA (rs = -0.301, p<0.0001), GPA (rs = -0.244, p = 0.001), TNF-α (rs = -0.242, p = 0.001) and IL-6 (rs = -0.145, p = 0.041)]. Likewise, 4cB12 was negatively associated with TC (rs = -0.203, p = 0.004) and LDL (rs = -0.222, p = 0.002) but positively correlated with HDL (rs = 0.196, p = 0.005). Conclusion: Vitamin B12 deficiency and diabetic neuropathy are very high among metformin-treated T2DM patients and it is associated with increased GPA, IFA, TNF-α and cardiometabolic risk factors (higher LDL and TC and lower HDL). Upon verification of these findings in a prospective case-control study, it may be beneficial to include periodic measurement of Vitamin B12 using the more sensitive combined indicators (4cB 12) in the management of patients with T2DM treated with metformin in Ghana. [ABSTRACT FROM AUTHOR]

Objective: The performance of a commercially available artificial intelligence (AI)-based software that detects breast arterial calcifications (BACs) on mammograms is presented., Methods: This retrospective study was exempt from IRB approval and adhered to the HIPAA regulations. Breast arterial calcification detection using AI was assessed in 253 patients who underwent 314 digital mammography (DM) examinations and 143 patients who underwent 277 digital breast tomosynthesis (DBT) examinations between October 2004 and September 2022. Artificial intelligence performance for binary BAC detection was compared with ground truth (GT) determined by the majority consensus of breast imaging radiologists. Area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value (NPV), accuracy, and BAC prevalence rates of the AI algorithm were compared., Results: The case-level AUCs of AI were 0.96 (0.93-0.98) for DM and 0.95 (0.92-0.98) for DBT. Sensitivity, specificity, and accuracy were 87% (79%-93%), 92% (88%-96%), and 91% (87%-94%) for DM and 88% (80%-94%), 90% (84%-94%), and 89% (85%-92%) for DBT. Positive predictive value and NPV were 82% (72%-89%) and 95% (92%-97%) for DM and 84% (76%-90%) and 92% (88%-96%) for DBT, respectively. Results are 95% confidence intervals. Breast arterial calcification prevalence was similar for both AI and GT assessments., Conclusion: Breast AI software for detection of BAC presence on mammograms showed promising performance for both DM and DBT examinations. Artificial intelligence has potential to aid radiologists in detection and reporting of BAC on mammograms, which is a known cardiovascular risk marker specific to women., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society of Breast Imaging.)

Background: Breast arterial calcification (BAC) on mammograms has emerged as a biomarker of women's cardiovascular disease (CVD) risk, but there is a lack of quantification tools and clinical outcomes studies., Objectives: This study assessed the association of BAC (both presence and quantity) with CVD outcomes., Methods: This single-center, retrospective study included women with a screening mammogram from 2007 to 2016. BAC was quantified using an artificial intelligence-generated score, which was assessed as both a binary and continuous variable. Regression analyses evaluated the association between BAC and mortality and a composite of acute myocardial infarction, heart failure, stroke, and mortality. Analyses were adjusted for age, race, diabetes, smoking, blood pressure, cholesterol, and history of CVD and chronic kidney disease., Results: A total of 18,092 women were included in this study (mean age 56.8 ± 11.0 years; diabetes [13%], hypertension [36%], hyperlipidemia [40%], and smoking [5%]). BAC was present in 4,223 (23%). Over a median follow-up of 6 years, death occurred in 7.8% and 2.3% of women with and without BAC, respectively. The composite occurred in 12.4% and 4.3% of women with and without BAC, respectively. Compared to those without, women with BAC had adjusted HRs of 1.49 (95% CI: 1.33-1.67) for mortality and 1.56 (95% CI: 1.41-1.72) for the composite. Each 10-point increase in the BAC score was associated with higher risk of mortality (HR: 1.08 [95% CI: 1.06-1.11]) and the composite (HR: 1.08 [95% CI: 1.06-1.10]). BAC was especially predictive of future events among younger women., Conclusions: BAC is independently associated with mortality and CVD, especially among younger women. Measurement of BAC beyond presence adds incremental risk stratification. Quantifying BAC using an artificial intelligence algorithm is feasible, clinically relevant, and may improve personalized CVD risk stratification., Competing Interests: This research was in part funded by the National Heart, Lung, and Blood Institute T32 HL079891-11. Drs Bui, Nerlekar, Eghtedari, and Daniels have served as consultants to CureMetrix. Drs Mantey and Wang are employees of CureMetrix. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (© 2024 The Authors.)