C-Peptide and BMi predict anti-hyperglycemic treatment lines in breast cancer patients treated with Alpelisib.

PURPOSE: Alpelisib is a PI3K (Phosphoinositide 3-kinases) inhibitor used for breast cancer which develops hyperglycemia based on its action on glucose metabolism regulation. This study aims to identify potential risk factors predicting hyperglycemia development and the need for multiple treatments for hyperglycemia in patients receiving Alpelisib. METHODS: Fourteen women diagnosed with metastatic hormone receptor-positive breast cancer carrying PI3K mutations who initiated treatment with Alpelisib were monitored through consultations in the Oncology and Endocrinology departments. Non-parametric ROC curves were generated to assess the need for three or more antidiabetic medications to achieve glycemic control. RESULTS: The study population had a median age of 64 years (range:48-69) with a median body mass index (BMI) of 26.6 kg/m2 (range: 22.9-29.4). Overweight was observed in 35.7% of the participants and obesity in 21.4%. Fifty percent of the participants had prediabetes, and 85.7% developed hyperglycemia requiring pharmacological treatment, although none of them needed to discontinue treatment for this reason. Baseline C-peptide levels and BMI were associated with the number of antidiabetic drugs used (Spearman's Rho 0.553, p = 0.040; Spearman's Rho 0.581, p = 0.030, respectively). ROC curve analysis showed and area under the curve (AUC) of 0.819 for the variable risk profile (defined as baseline C-peptide >10.5 ng/ml and BMI > 27 kg/m2), whereas AUC values were 0.556 and 0.514 for HbA1c and baseline glucose, respectively, (p = 0.012). CONCLUSION: A joint follow-up by an Oncology department and a Diabetes Unit can prevent treatment discontinuation in patients under Alpelisib therapy. Baseline BMI and plasma C-peptide levels can predict an increased need for anti-hyperglycemic treatment.

Impact of CFTR modulator therapy on body composition as assessed by thoracic computed tomography: A follow-up study.

OBJECTIVE: Treatment with cystic fibrosis transmembrane conductance regulator (CFTR) modulators in individuals with cystic fibrosis (CF) has brought a significant change in forced expiratory volume in 1 second (FEV1) and clinical parameters. However, it also results in weight gain. The aim of our study is to evaluate the effect of CFTR modulator treatment on body composition, measured by computed tomography (CT). METHODS: Adult subjects with CF under follow-up at La Princesa University Hospital were recruited. All of them were on elexacaftor-tezacaftor-ivacaftor (ELX/TEZ/IVA) treatment. Body composition analysis was conducted using CT scans and an open-source software. The results were then compared with bioimpedance estimations, as well as other clinical and spirometry data. RESULTS: Our sample consisted of 26 adult subjects. The fat mass compartments on CT scans correlated with similar compartments on bioimpedance, and normal-density muscle mass exhibited a strong correlation with phase angle. Higher levels of very low-density muscle prior to treatment were associated with lower final FEV1 and less improvement in FEV1 after therapy. We observed an increase in total body area (P < 0.001), driven by increases in total fat mass (P < 0.001), subcutaneous fat (P < 0.001), visceral fat (P = 0.002), and intermuscular fat (P = 0.022). The only muscle compartment that showed an increase after treatment was very low-density muscle (P = 0.032). CONCLUSIONS: CT scans represent an opportunity to assess body composition on CF. Combination treatment with CFTR modulators, leads to an improvement in FEV1 and to an increase in body mass in all compartments primarily at the expense of fat mass.

Effect of Christmas Holidays on Type 1 Diabetes Mellitus in Users of Glucose Flash Systems.

OBJECTIVE: Christmas holidays can impact weight and glycemic control in type 2 diabetes, but their effect on type 1 diabetes (T1D) remains understudied. This study assessed how Christmas holidays affect individuals with T1D who use flash continuous glucose monitoring systems. METHODS: This retrospective study involved 812 adults diagnosed with T1D recruited from 3 hospitals. Clinical, anthropometric, and socioeconomic data were collected. Glucose metrics from 14 days before January 1st, and before December 1st and February 1st as control periods, were recorded. Analyses adjusted for multiple variables were conducted to assess the holiday season's impact on glycemic control. RESULTS: The average time in range during the holidays (60.0 ± 17.2%) was lower compared to December (61.9 ± 17.2%, P < .001) and February (61.7 ± 17.7%, P < .001). Time above range (TAR > 180 mg/dL) was higher during Christmas (35.8 ± 18.2%) compared to December (34.1 ± 18.3%, P < .001) and February (34.2 ± 18.4%, P < .001). Differences were also observed in TAR >250 mg/dL, coefficient of variation, and average glucose (P < .05). No differences were found in time below range or other metrics. Linear regression models showed that the holidays reduced time in range by 1.9% (ß = -1.92, P = .005) and increased TAR >180 mg/dL by 1.8% (ß = 1.75, P = .016). CONCLUSION: Christmas holidays are associated with a mild and reversible deterioration in glucose metrics among individuals with T1D using flash continuous glucose monitoring, irrespective of additional influencing factors. These discoveries can be useful to advise individuals with diabetes during the festive season and to recognize potential biases within studies conducted during this timeframe.

Impact of socioeconomic status on chronic control and complications of type 1 diabetes mellitus in users of glucose flash systems: a follow-up study.

BACKGROUND: This study investigates the association between socioeconomic status (SES) and glycemic control in individuals with type 1 diabetes (T1D) using flash glucose monitoring (FGM) devices within a public health system where these technologies are freely available and utilized according to recommended guidelines. METHODS: A follow-up study of 1060 adults (mean age 47.4 ± 15.0 years, 49.0% women) with T1D, receiving care at three Spanish university hospitals that regularly employ the FGM system. SES was assessed using the Spanish Deprivation Index and the average annual net income per person. Glycemic data were collected over a 14-day follow-up period, including baseline glycated hemoglobin (HbA1c) levels prior to sensor placement, the last available HbA1c levels, and FGM-derived glucose metrics. Individuals with sensor usage time < 70% were excluded. Chronic micro and macrovascular complications related to diabetes were documented. Regression models, adjusted for clinical variables, were employed to determine the impact of SES on optimal sensor control (defined as time in range (TIR) ≥ 70% with time below range < 4%) and disease complications. RESULTS: The average follow-up was of 2 years. The mean TIR and the percentage of individuals with optimal control were higher in individuals in the highest SES quartile (64.9% ± 17.8% and 27.9%, respectively) compared to those in the lowest SES quartile (57.8 ± 17.4% and 12.1%) (p < 0.001). Regression models showed a higher risk of suboptimal control (OR 2.27, p < 0.001) and ischemic heart disease and/or stroke (OR 3.59, p = 0.005) in the lowest SES quartile. No association was observed between SES and the risk of diabetic nephropathy and retinopathy. FGM system improved HbA1c levels across all SES quartiles. Although individuals in the highest SES quartile still achieved a significantly lower value at the end of the follow-up 55 mmol/mol (7.2%) compared to those in the lowest SES quartile 60 mmol/mol (7.6%) (p < 0.001), the significant disparities in this parameter between the various SES groups were significantly reduced after FGM technology use. CONCLUSIONS: Socioeconomic status plays a significant role in glycemic control and complications in individuals with T1D, extending beyond access to technology and its proper utilization. The free utilization of FGM technology helps alleviate the impact of social inequalities on glycemic control.

Scans per day as predictors of optimal glycemic control in people with type 1 diabetes mellitus using flash glucose monitoring: what number of scans per day should raise a red flag?

AIMS: This study aimed to determine the minimum frequency of flash glucose monitoring (FGM) scans necessary for optimal glycemic control in patients with type 1 diabetes (T1D). METHODS: Data were collected from 692 patients (47.5% female, with a median age of 47.4 years) who used FGM systems daily and recorded their clinical variables and device data. RESULTS: Logistic regression models showed that performing more than 12 scans per day was associated with improved T1D control (OR = 4.22, p < 0.001) and a reduction in HbA1c (7.6 vs 7.0%, 60-53 mmol/mol p < 0.001). However, those performing less than 6 scans showed no improvement in HbA1c (7.9 vs 7.8%, 63-61 mmol/mol p = 0.514). Thirteen daily scans were determined as the optimal cutoff point for predicting optimal glycemic control using a maximally selected rank algorithm. Significant reductions were observed in mean glucose (< 0.001), coefficient of variation (< 0.001), HbA1c (< 0.001), and an increase in TIR (< 0.001) in patients who performed more than 12 daily scans. CONCLUSIONS: The results suggest that a higher frequency of daily scans by T1D patients using FGM systems leads to improved chronic glycemic control. The minimum recommended frequency for optimal control is 13 scans per day, and more than 6 daily scans are needed to improve HbA1c.

Patient satisfaction in three advanced hybrid closed-loop systems at 6 months of treatment in adults with type 1 diabetes mellitus: a follow-up study.

INTRODUCTION: Advanced hybrid closed-loop (AHCL) systems have demonstrated improved glycemic control in individuals with Type 1 Diabetes Mellitus. The aim of this study is to compare patient satisfaction among three available AHCL systems (Medtronic Minimed780 G, Roche Diabeloop DBLG1, and Tandem t:slim X2 Control IQ) after six months of treatment and to determine if it is related to glycemic control. METHODS: The data of 75 individuals were analyzed, including 15 using the DBLG1 system, 9 using Control IQ, and 51 using MM780 G. Patient satisfaction was assessed using the Diabetes Treatment Satisfaction Questionnaire for Diabetes Mellitus (DTSQc), a validated instrument. RESULTS: All systems demonstrated treatment satisfaction. The DBLG-1 system scored 14 (-15-21) points, while Control IQ scored 21 (9-24) and M780 G scored 19 (11-24) (p = 0.004). The multivariate analysis revealed that the DBLG-1 system is associated with a lower DTSQc score (OR 0.19, p = 0.019) independent of glycemic control, sex, age, duration of diabetes, duration as an insulin pump user, and daily insulin dose. CONCLUSION: AHCL systems are satisfactory treatments for users, with potential variations observed between each system regardless of the achieved glycemic control.