Adiposity Predicts Cognitive Decline in Older Persons with Diabetes: A 2-Year Follow-Up

BACKGROUND: The mechanisms related to cognitive impairment in older persons with Type 2 diabetes (DM) remains unclear. We tested if adiposity parameters and body fat distribution could predict cognitive decline in older persons with DM vs. normal glucose tolerance (NGT). METHODOLOGY: 693 older persons with no dementia were enrolled: 253 with DM in good metabolic control; 440 with NGT (age range:65-85 years). Longitudinal study comparing DM and NGT individuals according to the association of baseline adiposity parameters (body mass index (BMI), waist-hip-ratio (WHR), waist circumference (WC) and total body fat mass) to cognitive change (Mini Mental State Examination (MMSE), a composite score of executive and attention functioning (CCS) over time. FINDINGS: At baseline, in DM participants, MMSE correlated with WHR (beta = -0.240; p = 0.043), WC (beta = -0.264; p = 0.041) while CCS correlated with WHR (beta = -0.238; p = 0.041), WC (beta = -0.326; p = 0.013) after adjusting for confounders. In NGT subjects, no significant correlations were found among any adiposity parameters and MMSE, while CCS was associated with WHR (beta = -0.194; p = 0.036) and WC (beta = -0.210; p = 0.024). Participants with DM in the 3(rd) tertile of total fat mass showed the greatest decline in cognitive performance compared to those in 1(st) tertile (tests for trend: MMSE(p = 0.007), CCS(p = 0.003)). Logistic regression models showed that 3(rd) vs. 1(st) tertile of total fat mass, WHR, and WC predicted an almost two-fold decline in cognitive function in DM subjects at 2(nd) yr (OR 1.68, 95%IC 1.08-3.52). CONCLUSIONS: Total fat mass and central adiposity predict an increased risk for cognitive decline in older person with DM

"Delirium Day": A nationwide point prevalence study of delirium in older hospitalized patients using an easy standardized diagnostic tool

Background: To date, delirium prevalence in adult acute hospital populations has been estimated generally from pooled findings of single-center studies and/or among specific patient populations. Furthermore, the number of participants in these studies has not exceeded a few hundred. To overcome these limitations, we have determined, in a multicenter study, the prevalence of delirium over a single day among a large population of patients admitted to acute and rehabilitation hospital wards in Italy. Methods: This is a point prevalence study (called "Delirium Day") including 1867 older patients (aged 65 years or more) across 108 acute and 12 rehabilitation wards in Italian hospitals. Delirium was assessed on the same day in all patients using the 4AT, a validated and briefly administered tool which does not require training. We also collected data regarding motoric subtypes of delirium, functional and nutritional status, dementia, comorbidity, medications, feeding tubes, peripheral venous and urinary catheters, and physical restraints. Results: The mean sample age was 82.0 ± 7.5 years (58 % female). Overall, 429 patients (22.9 %) had delirium. Hypoactive was the commonest subtype (132/344 patients, 38.5 %), followed by mixed, hyperactive, and nonmotoric delirium. The prevalence was highest in Neurology (28.5 %) and Geriatrics (24.7 %), lowest in Rehabilitation (14.0 %), and intermediate in Orthopedic (20.6 %) and Internal Medicine wards (21.4 %). In a multivariable logistic regression, age (odds ratio [OR] 1.03, 95 % confidence interval [CI] 1.01-1.05), Activities of Daily Living dependence (OR 1.19, 95 % CI 1.12-1.27), dementia (OR 3.25, 95 % CI 2.41-4.38), malnutrition (OR 2.01, 95 % CI 1.29-3.14), and use of antipsychotics (OR 2.03, 95 % CI 1.45-2.82), feeding tubes (OR 2.51, 95 % CI 1.11-5.66), peripheral venous catheters (OR 1.41, 95 % CI 1.06-1.87), urinary catheters (OR 1.73, 95 % CI 1.30-2.29), and physical restraints (OR 1.84, 95 % CI 1.40-2.40) were associated with delirium. Admission to Neurology wards was also associated with delirium (OR 2.00, 95 % CI 1.29-3.14), while admission to other settings was not. Conclusions: Delirium occurred in more than one out of five patients in acute and rehabilitation hospital wards. Prevalence was highest in Neurology and lowest in Rehabilitation divisions. The "Delirium Day" project might become a useful method to assess delirium across hospital settings and a benchmarking platform for future surveys

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

Polluino: An efficient cloud-based management of IoT devices for air quality monitoring

The Internet of Things paradigm originates from the proliferation of intelligent devices that can sense, compute and communicate data streams in a ubiquitous information and communication network. The great amounts of data coming from these devices introduce some challenges related to the storage and processing capabilities of the information. This strengthens the novel paradigm known as Big Data. In such a complex scenario, the Cloud computing is an efficient solution for the managing of sensor data. This paper presents Polluino, a system for monitoring the air pollution via Arduino. Moreover, a Cloud-based platform that manages data coming from air quality sensors is developed

Dipeptidyl Peptidase 4 InhibitionMay Facilitate Healing of Chronic Foot Ulcers in Patients with Type 2 Diabetes

The pathophysiology of chronic diabetic ulcers is complex and still incompletely understood, both micro- and macroangiopathy strongly contribute to the development and delayed healing of diabetic wounds, through an impaired tissue feeding and response to ischemia. With adequate treatment, some ulcers may last only weeks; however, many ulcers are difficult to treat and may last months, in certain cases years; 19–35% of ulcers are reported as nonhealing. As no efficient therapy is available, it is a high priority to develop new strategies for treatment of this devastating complication. Because experimental and pathological studies suggest that incretin hormone glucagon-like peptide-1 may improves VEGF generation and promote the upregulation of HIF-1α through a reduction of oxidative stress, the study evaluated the effect of the augmentation of GLP-1, by inhibitors of the dipeptidyl peptidase-4, such as vildagliptin, on angiogenesis process and wound healing in diabetic chronic ulcers. Although elucidation of the pathophysiologic importance of these aspects awaits further confirmations, the present study evidences an additional aspect of how DPP-4 inhibition might contribute to improved ulcer outcome

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies

: The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information.Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach.In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies