Insulin resistance during androgen deprivation therapy in men with prostate cancer.

BACKGROUND: Androgen deprivation therapy (ADT) in prostate cancer (PCa) has been associated with development of insulin resistance. However, the predominant site of insulin resistance remains unclear. METHODS: The ADT & Metabolism Study was a single-center, 24-week, prospective observational study that enrolled ADT-naive men without diabetes who were starting ADT for at least 24 weeks (ADT group, n = 42). The control group comprised men without diabetes with prior history of PCa who were in remission after prostatectomy (non-ADT group, n = 23). Prevalent diabetes mellitus was excluded in both groups using all three laboratory criteria defined in the American Diabetes Association guidelines. All participants were eugonadal at enrollment. The primary outcome was to elucidate the predominant site of insulin resistance (liver or skeletal muscle). Secondary outcomes included assessments of body composition, and hepatic and intramyocellular fat. Outcomes were assessed at baseline, 12, and 24 weeks. RESULTS: At 24 weeks, there was no change in hepatic (1.2; 95% confidence interval [CI], -2.10 to 4.43; p = .47) or skeletal muscle (-3.2; 95% CI, -7.07 to 0.66; p = .10) insulin resistance in the ADT group. No increase in hepatic or intramyocellular fat deposition or worsening of glucose was seen. These changes were mirrored by those observed in the non-ADT group. Men undergoing ADT gained 3.7 kg of fat mass. CONCLUSIONS: In men with PCa and no diabetes, 24 weeks of ADT did not change insulin resistance despite adverse body composition changes. These findings should be reassuring for treating physicians and for patients who are being considered for short-term ADT.

Impact of personalized diabetes care on distress and treatment satisfaction in people with breast cancer.

AIMS: In patients with breast cancer (BCa) and diabetes (DM), diabetes distress (DD) and treatment satisfaction (DTS) can influence BCa management and outcomes. We assessed the impact of implementing a personalized diabetes care model in patients with BCa. METHODS: Patients in active treatment or surveillance for BCa with an HbA1c > 53 mmol/mol (7%) or random blood glucose >11.1 mmol/L were included. Participants were offered continuous glucose monitoring (CGM), virtual care and a dedicated diabetes provider for 6 months. Primary outcomes included DD measured by the Diabetes Distress Survey (DDS) and DTS measured by the Diabetes Treatment Satisfaction Questionnaire (DTSQ). Questionnaires were conducted at 0, 3 and 6 months. RESULTS: Thirty-one women were enrolled (median age 61, IQR 49.0-69.0). Compared to baseline, the mean DDS score was lower at both 3 months (2.2 vs. 1.8 [n = 27], p = 0.004, SD = 0.70) and 6 months (2.3 vs. 1.8 [n = 23], p = 0.002, SD = 0.70). The mean DTSQ score was higher at 3 months (baseline: 20.5 vs. 3 months: 28.7 [n = 28], p < 0.001, SD = 9.2) and 6 months (baseline: 20.4 vs. 6 months: 30.0 [n = 26], p < 0.001, SD = 9.7). CONCLUSIONS: Personalized diabetes care models that emphasize remote management and optimize access for those with BCa may lower DD and improve DTS.

Factors leading to alpelisib discontinuation in patients with hormone receptor positive, human epidermal growth factor receptor-2 negative breast cancer.

PURPOSE: Alpelisib is a phosphoinositide-3-kinase inhibitor approved for hormone-receptor-positive, PIK3CA-mutated metastatic breast cancer. However, length of drug exposure, maximum-tolerated dose, and therefore clinical response can vary significantly outside of the trial setting. This study evaluates our center's "real world" experience with alpelisib and focuses on duration of therapy and factors associated with cancer progression. METHODS: Patients receiving alpelisib at our center between 2019 and 2021 were identified. We evaluated duration of alpelisib therapy and the causative reasons for drug discontinuation. The association of drug duration and dose with subsequent cancer progression were assessed, along with the association between hyperglycemia during alpelisib therapy and cancer progression. RESULTS: Sixty-two women prescribed alpelisib were included (mean age 61 years). Disease progression was the most common reason for drug discontinuation, while discontinuation within 30 days was primarily attributed to adverse events (AEs). Among those who progressed, median time to progression was longer in those on alpelisib for > 90 days compared with those on alpelisib for ≤ 90 days (187 vs. 77 days, p < 0.001). At 200 days, freedom from progression was greater for those on alpelisib for > 90 days compared to those receiving therapy for ≤ 90 days (59% vs. 19%, p = 0.001). Median blood glucose as a continuous variable was associated with disease progression (HR 1.01, 95% CI 1.00-1.02, p = 0.02). CONCLUSION: While progression of disease is the largest contributor to alpelisib discontinuation, AEs are the leading cause for early drug cessation. Shorter alpelisib exposure is associated with greater cancer progression. Further studies are needed to determine the impact of sustained hyperglycemia on cancer progression.

Outreach Method Predicts Patient Re-engagement in Diabetes Care During Sustained Care Disruption.

OBJECTIVE: During the COVID-19 pandemic, visits for diabetes care were abruptly canceled without predefined procedures to re-engage patients. This study was designed to determine how outreach influences patients to maintain diabetes care and identify factors that might impact the intervention's efficacy. METHODS: A diabetes nursing team attempted outreach for patients who had a canceled appointment for diabetes between March 16, 2020, and June 19, 2020. Outreach status was defined as reached, message left, or no contact. Outcomes were defined as follows: (1) booking and (2) keeping a follow-up appointment. RESULTS: Seven hundred eighty-seven patients were included (384 [49%] were reached, 152 (19%) were left a message, and 251 (32%) had no contact). Reached patients were more likely to book [odds ratio (OR) = 2.43, P < .001] and keep an appointment (OR = 2.39, P < .001) than no-contact patients. Leaving a message did not increase the odds of booking (OR = 1.05, P = .84) or keeping (OR = 1.17, P = .568) an appointment compared with no contact. Older age was a significant predictor of booking an appointment (OR = 1.014 for each year of age, P = .037). Patients on insulin were more likely to keep their appointment (OR = 1.70, P = .008). Patients with a higher hemoglobin A1C level were less likely to keep their appointment (OR = 0.87 for each 1.0% increase in the hemoglobin A1C level, P = .011). CONCLUSION: These findings suggest that to optimize re-engagement during care disruption, 1-way communication is no better than no contact and that 2-way communication increases the likelihood that patients will maintain access to care. In addition, although higher-risk patients (eg, patients with older age or those on insulin) may be more incentivized to stay engaged, targeted outreach is needed for those with chronically poor glycemic control.

Patient-Centered Diabetes Care of Cancer Patients.

PURPOSE OF REVIEW: There is a bidirectional relationship between cancer and diabetes, with one condition influencing the prognosis of the other. Multiple cancer therapies cause diabetes including well-established medications such as glucocorticoids and novel cancer therapies such as immune checkpoint inhibitors (CPIs) and phosphoinositide 3-kinase (PI3K) inhibitors. RECENT FINDINGS: The nature and severity of diabetes caused by each therapy differ, with some predominantly mediated by insulin resistance, such as PI3K inhibitors and glucocorticoids, while others by insulin deficiency, such as CPIs. Studies have demonstrated diabetes from CPIs to be more rapidly progressing than conventional type 1 diabetes. There remains a scarcity of published guidance for the screening, diagnosis, and management of hyperglycemia and diabetes from these therapies. The need for such guidance is critical because diabetes management in the cancer patient is complex, individualized, and requires inter-disciplinary care. In the present narrative review, we synthesize and summarize the most relevant literature pertaining to diabetes and hyperglycemia in the setting of these cancer therapies and provide an updated patient-centered framework for their evaluation and management.

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study.

BACKGROUND: Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. OBJECTIVE: This study aims to understand patients' preferences for mobile health (mHealth) technology and how that technology can be integrated into patients' routines, especially with regard to medication use. METHODS: We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication-taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. RESULTS: We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants' mean glycated hemoglobin A1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. CONCLUSIONS: The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

Telemedicine in Complex Diabetes Management.

PURPOSE OF REVIEW: Telehealth has the potential to positively transform the quality and cost-effectiveness of complex diabetes management in adults. This review explores the landscape of telemedicine approaches and evidence for incorporation into general practice. RECENT FINDINGS: Telemedicine for diabetes care is feasible based on over 100 randomized clinical trials. Evidence shows modest benefits in A1c lowering and other clinical outcomes that are better sustained over time vs. usual care. While telemedicine interventions are likely cost-effective in diabetes care, more research is needed using implementation science approaches. Telehealth platforms have been shown to be both feasible and effective for health care delivery in diabetes, although there are many caveats that require tailoring to the institution, clinician, and patient population. Research in diabetes telehealth should focus next on how to increase access to patients who are known to be marginalized from traditional models of health care.

Chronic Care Management Services for Complex Diabetes Management: a Practical Overview.

PURPOSE OF REVIEW: Formalized chronic care management has the potential to improve the quality and cost-effectiveness of complex diabetes management in adults, but has historically not been sustainably supported by health care systems. This review discusses the application of the chronic care model in the care of complex diabetes and its translation in the current reimbursement structure designed by Centers for Medicare and Medicaid Services (CMS). RECENT FINDINGS: Following the introduction of Wagner's Chronic Care Model (CCM) in the late 1990s, evidence gathered over the past 2 decades has supported the shift in focus of health care systems from acute to chronic disease management and proactive care. Acknowledging evidence and potential for improved cost-effectiveness, in 2015, Medicare began reimbursing for chronic care management services (CCMS) for patients with multiple chronic conditions. The CCMS billing codes allow a program to be reimbursed for up to 90 min per month spent by clinical staff performing interim care within a comprehensive care plan. Recent data from local and global programs support the application of formalized CCM in diabetes management. Although reimbursement models for CCM have been designed for use in primary care, the challenges of the reimbursement model has opened the door for specialty areas focused on multimorbidity care such as diabetes care to explore this approach. With the broader availability of remote glucose monitoring and telemedicine, a strategy that combines goal-oriented care and telehealth solutions appears to be most effective in diabetes CCM care. Despite widespread acceptance of the chronic care model of care, there remain significant barriers to its incorporation into standard practice.

Global Corner Spotlight: Perspectives on Diabetes Care in Cambodia as Seen Through an Ngo-Run Healthcare System.

ABBREVIATIONS: CMC = Community Medical Center; SGLT2 = sodium-glucose cotransporter 2; SHCH = Sihanouk Hospital Center of Hope.

REDUCED IMPACT OF DIABETES CLINIC REFERRAL ON HIGH-FREQUENCY EMERGENCY DEPARTMENT USERS.

OBJECTIVE: The patterns of emergency department (ED) visits in patients with diabetes are not well understood. The Emergency Department Diabetes Rapid-referral Program (EDRP) allows direct booking of ED patients presenting with urgent diabetes needs into a diabetes specialty clinic within 1 day of ED discharge. The objective of this secondary analysis was to examine characteristics of patients with diabetes who have frequent ED visits and determine reasons for revisits. METHODS: A single-center analysis was conducted comparing patients referred to the EDRP (n = 420) to historical unexposed controls (n = 791). The primary outcome was the proportion of patients in each frequency group of ED revisits (none, 1 to 3 [infrequent], 4 to 10 [frequent], or >10 [superfrequent]) in the year after the ED index visit. Secondary outcomes were hospitalization rates and International Classification of Diseases-Ninth Revision (ICD-9) diagnoses at ED revisits. RESULTS: Superfrequent users, responsible for >20% of total ED visits, made up small but not significantly different proportions of EDRP and control populations, 3.6% and 5.2%, respectively. Superfrequent groups had lower hospital admission rates at ED revisits compared to frequent groups. Mental health disorders (including substance abuse) were the primary, secondary, or tertiary ICD-9 codes in 30.6% (95% confidence interval [CI], 27.7% to 33.5%) and 6.6% (95% CI, 5.1% to 8.2%) in the superfrequent and infrequent groups, respectively. CONCLUSION: Direct access to diabetes specialty care from the ED is effective in reducing ED recidivism but not amongst a small subgroup of superfrequent ED users. This group was more likely to have mental health disorders recorded at ED revisits, suggesting that more comprehensive approaches are needed for this population. ABBREVIATIONS: EDRP = Emergency Department Diabetes Rapid-referral Program; ED = emergency department; HbA1c = hemoglobin A1c; ICD-9 = International Classification of Diseases-Ninth Revision.

GLYCEMIC OUTCOMES 3 YEARS AFTER IMPLEMENTATION OF A PERI-OPERATIVE GLYCEMIC CONTROL ALGORITHM IN AN ACADEMIC INSTITUTION.

OBJECTIVE: While hyperglycemia in the postoperative setting has been linked to an increase in surgical complications, limited data are available to inform the management of patients with diabetes in the operating room and the immediate peri-operative period. We describe the results of a peri-operative glycemic control program that standardized intravenous insulin with a target glucose (BG) range of 120 to 180 mg/dL for patients with diabetes presenting with a BG level >180 mg/dL and included transition to subcutaneous insulin. METHODS: Patients with known diabetes and a BG >180 mg/dL who underwent surgery were included. The control group included 260 patients from March 2, 2008 through December 31, 2008. The intervention group included 588 patients following protocol implementation from April 1, 2009 through December 31, 2012. Data included demographic information, hospital BG values, length of stay (LOS), mortality, and wound infections. RESULTS: The intervention group had significantly lower BG on arrival in the postoperative care unit (182.2 vs 194.9 mg/dL, P = .012). Mean BG during the first 24 hours after surgery was lower in the intervention group (182.1 vs. 190.5 mg/dL), and there were fewer BG values >200 mg/dL in the intervention group (P = .005). The percentage of BG values <70 mg/dL during the hospital stay was lower in the intervention group (1.94 vs. 2.43%, P<.01). There was no significant difference in mortality, LOS, or wound infections. CONCLUSION: Following implementation of a hospital-wide peri-operative glycemic control algorithm, we found a reduction in peri-operative BG levels and hypoglycemia rates. Ongoing research is needed to assess the impact on clinical outcomes. ABBREVIATIONS: BG = blood glucose CCI = Charlson comorbidity index EHR = electronic health record ICD-9 = International Classification of Disease-9 IV = intravenous LOS = length of stay OR = operating room PACU = postoperative care unit POC = point-of-care.

Stress Hyperglycemia During Surgery and Anesthesia: Pathogenesis and Clinical Implications.

Numerous studies have demonstrated an association between hyperglycemia in the perioperative period and adverse clinical outcomes. Many patients who experience hyperglycemia while hospitalized do not have a known history of diabetes and experience a transient phenomenon often described as "stress hyperglycemia" (SH). We discuss the epidemiology and pathogenesis of SH as well as evidence to date regarding predisposing factors and outcomes. Further research is needed to identify the long-term sequelae of SH as well as perioperative measures that may modulate glucose elevations and optimal treatment strategies.

DEVELOPMENT AND VALIDATION OF A NOVEL TOOL TO PREDICT HOSPITAL READMISSION RISK AMONG PATIENTS WITH DIABETES.

OBJECTIVE: To develop and validate a tool to predict the risk of all-cause readmission within 30 days (30-d readmission) among hospitalized patients with diabetes. METHODS: A cohort of 44,203 discharges was retrospectively selected from the electronic records of adult patients with diabetes hospitalized at an urban academic medical center. Discharges of 60% of the patients (n = 26,402) were randomly selected as a training sample to develop the index. The remaining 40% (n = 17,801) were selected as a validation sample. Multivariable logistic regression with generalized estimating equations was used to develop the Diabetes Early Readmission Risk Indicator (DERRI™). RESULTS: Ten statistically significant predictors were identified: employment status; living within 5 miles of the hospital; preadmission insulin use; burden of macrovascular diabetes complications; admission serum hematocrit, creatinine, and sodium; having a hospital discharge within 90 days before admission; most recent discharge status up to 1 year before admission; and a diagnosis of anemia. Discrimination of the model was acceptable (C statistic 0.70), and calibration was good. Characteristics of the validation and training samples were similar. Performance of the DERRI™ in the validation sample was essentially unchanged (C statistic 0.69). Mean predicted 30-d readmission risks were also similar between the training and validation samples (39.3% and 38.7% in the highest quintiles). CONCLUSION: The DERRI™ was found to be a valid tool to predict all-cause 30-d readmission risk of individual patients with diabetes. The identification of high-risk patients may encourage the use of interventions targeting those at greatest risk, potentially leading to better outcomes and lower healthcare costs. ABBREVIATIONS: DERRI™ = Diabetes Early Readmission Risk Indicator ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification GEE = generalized estimating equations ROC = receiver operating characteristic.

HYPOGLYCEMIA RATES AFTER RESTRICTION OF HIGH-DOSE GLARGINE IN HOSPITALIZED PATIENTS.

OBJECTIVE: Hypoglycemia remains one of the main challenges of insulin therapy. To reduce insulin-related hypoglycemia at our institution, we restricted inpatient ordering of high glargine doses (≥0.5 U/kg/day) to endocrine staff in May 2013. This retrospective cohort study assesses its effect on hypoglycemia and glycemic control within 48 hours of admission (ADM). METHODS: We identified 692 adult patients hospitalized at Boston Medical Center who received glargine upon ADM from November 1, 2012 through April 30, 2013 as the pre-intervention group, and 651 adult patients admitted between November 1, 2013 and April 30, 2014 as the postintervention group. Demographics, medical history, home insulin regimen, concurrent oral diabetes medications or glucocorticoid administration, ADM serum creatinine, all blood glucose levels (BG) ≤48 hours of ADM, and hemoglobin A1c values ≤3 months were assessed. Hypoglycemia was defined as BG ≤70 mg/dL, and hyperglycemia as BG ≥200 mg/dL. Multivariable regression models assessed potential associations between covariates and incidence of hypoglycemia and average BG ≤48 hours of ADM. RESULTS: Demographics were similar between groups. Significantly less patients received high-dose glargine in the post-intervention group (5.2% vs. 0.3%, P<.001). Incidences of hypoglycemia were significantly lower in the postintervention group (20.9% vs. 17.8%, P<.001 per ADM; 3.4% vs. 2.3%, P = .001 per BG measurements [BGM]). Mean BG levels ≤48 hours of ADM and incidence of hyperglycemia were not significantly different. The adjusted incident rate ratio of hypoglycemia was 0.63 per ADM and 0.74 per BGM in the postintervention group compared to the pre-intervention group (P = .001 and P = .063, respectively). CONCLUSION: We found that implementation of a restriction on high doses of glargine resulted in lower rates of hypoglycemia without worsening glycemic control. ABBREVIATIONS: ADM = admission BG = blood glucose BGM = blood glucose measurements BMC = Boston Medical Center BMI = body mass index EMR = electronic medical record HgbA1c = hemoglobin A1c IRR = incidence rate ratio NPH = neutral protamine Hagedorn TDD = total daily dose T2D = type 2 diabetes.

OPEN ACCESS TO DIABETES CENTER FROM THE EMERGENCY DEPARTMENT REDUCES HOSPITALIZATIONS IN THE SUSEQUENT YEAR.

OBJECTIVE: Patients who present to the emergency department (ED) for diabetes without hyperglycemic crisis are at risk of unnecessary hospitalizations and poor outcomes. To address this, the ED Diabetes Rapid-referral Program (EDRP) was designed to provide ED staff with direct booking into the diabetes center. The objective of this study was to determine the effects of the EDRP on hospitalization rate, ED utilization rate, glycemic control, and expenditures. METHODS: We conducted a single-center analysis of the EDRP cohort (n = 420) and compared 1-year outcomes to historic controls (n = 791). We also compared EDRP patients who arrived (ARR) to those who did not show (NS). The primary outcome was hospitalization rate over 1 year. Secondary outcomes included ED recidivism rate, hemoglobin A1c (HbA1c), and healthcare expenditures. RESULTS: Compared with controls, the EDRP cohort was less likely to be hospitalized (27.1% vs. 41.5%, P<.001) or return to the ED (52.2% vs. 62.3%, P = .001) at the end of 1 year. Total hospitalizations were also lower in the EDRP (157 ± 19 vs. 267 ± 18 per 1,000 persons per year, P<.001). The EDRP cohort had a greater reduction in HbA1c (-2.66 vs. -2.01%, P<.001), which was more pronounced when ARR patients were compared with NS (-2.71% vs. -1.37%, P<.05). The mean per patient institutional healthcare expenditures were lower by $5,461 compared with controls. CONCLUSION: Eliminating barriers to scheduling diabetes-focused ambulatory care for ED patients was associated with significant reductions in hospitalization rate, ED recidivism rate, HbA1c, and healthcare expenditures in the subsequent year. ABBREVIATIONS: ARR = arrived ED = emergency department EDRP = emergency department diabetes rapid-referral Program HbA1c = hemoglobin A1c NS = no show.

B cells promote inflammation in obesity and type 2 diabetes through regulation of T-cell function and an inflammatory cytokine profile.

Patients with type 2 diabetes (T2D) have disease-associated changes in B-cell function, but the role these changes play in disease pathogenesis is not well established. Data herein show B cells from obese mice produce a proinflammatory cytokine profile compared with B cells from lean mice. Complementary in vivo studies show that obese B cell-null mice have decreased systemic inflammation, inflammatory B- and T-cell cytokines, adipose tissue inflammation, and insulin resistance (IR) compared with obese WT mice. Reduced inflammation in obese/insulin resistant B cell-null mice associates with an increased percentage of anti-inflammatory regulatory T cells (Tregs). This increase contrasts with the sharply decreased percentage of Tregs in obese compared with lean WT mice and suggests that B cells may be critical regulators of T-cell functions previously shown to play important roles in IR. We demonstrate that B cells from T2D (but not non-T2D) subjects support proinflammatory T-cell function in obesity/T2D through contact-dependent mechanisms. In contrast, human monocytes increase proinflammatory T-cell cytokines in both T2D and non-T2D analyses. These data support the conclusion that B cells are critical regulators of inflammation in T2D due to their direct ability to promote proinflammatory T-cell function and secrete a proinflammatory cytokine profile. Thus, B cells are potential therapeutic targets for T2D.

Metformin may be associated with false-negative cancer detection in the gastrointestinal tract on PET/CT.

OBJECTIVE: Concurrent therapy with the antihyperglycemic drug metformin can hinder the detection of malignancy in the abdominal and pelvic portions of 18F-fluordeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging performed for the diagnosis or staging of malignancy, as well as for treatment response and radiation therapy planning. This is due to the metformin-induced increase in intestinal FDG radiotracer uptake. We aim to bring this potentially important interaction to the attention of clinicians who care for cancer patients with diabetes. METHODS: We searched MEDLINE (from 1970 to January 2014) and Google Scholar for relevant English-language articles using the following search terms: "metformin and FDG/PET, metformin and bowel uptake, metformin, and cancer, metformin and the intestine, metformin pharmacokinetics, hyperglycemia and FDG/PET." We reviewed the reference lists of pertinent articles with respect to metformin gut physiology, impact on FDG uptake and the effect on diagnostic accuracy of abdominalpelvic PET/CT scans with concurrent metformin therapy. RESULTS: We reviewed the action of metformin in the intestine, with particular emphasis on the role of metformin in PET/CT imaging and include a discussion of clinical studies on the topic to help refine knowledge and inform practice. Finally, we discuss aspects pertinent to the management of type 2 diabetes (T2D) patients on metformin undergoing PET/CT. CONCLUSIONS: Metformin leads to intense, diffusely increased FDG uptake in the colon, and to a lesser degree, the small intestine, which limits the diagnostic capabilities of FDG PET/CT scanning and may mask gastrointestinal malignancies. We suggest that metformin be discontinued 48 hours before FDG PET/CT scanning is performed in oncology patients. More rigorous data are needed to support the widespread generalizability of this recommendation.