Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer's disease.

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer's Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Clinical features, diagnostic criteria and pathogenesis of diabetes mellitus.

Diabetes mellitus is a metabolic disorder of glucose homeostasis and associated with long-term vascular complications leading to morbidity and mortality. It is the fastest growing non-communicable disease throughout the world. The pathophysiology of diabetes is complex and multifactorial. Understanding pathological mechanisms of disease can help clinicians to identify and treat the factors involved effectively, and design preventive strategies so as to halt the pandemic of this deadly disorder.

Management and treatment of diabetes mellitus.

Management of patients with Type 2 diabetes mellitus (T2DM) demands a comprehensive approach which includes diabetes education, an emphasis on life style modification, achievement of good glycemic control, minimization of cardiovascular risk, and avoidance of drugs that can aggravate glucose or lipid metabolism, and screening for diabetes complications. Comprehensive diabetes management can delay the progression of complication and maximize the quality of life. Acquiring knowledge about diabetes is an essential part of diabetes management, and even more important is to make the patient aware of this chronic disease. "For a diabetic patient, knowledge and understanding are not a part of treatment--they are the treatment".

Gestational diabetes mellitus.

Gestational diabetes mellitus is one of the most common medical problems that results from an increase in the insulin resistance as well as an impairment of the compensatory increase in insulin secretion from the beta cells of the pancreas. It serves as a metabolic stress test that uncovers underlying insulin resistance and beta-cell dysfunction. Gestational diabetes is associated with a variety of maternal and fetal complications, most notably macrosomia. Controversy surrounds the ideal approach for detecting gestational diabetes, andthe approaches recommended for screening and diagnosis are largely based on expert opinion. Controlling maternal glycemia with Medical Nutrition Therapy, close monitoring of blood glucose levels and treatment with insulin if blood glucose levels are not at goal has been shown to decrease fetal and maternal morbidities. Other treatment modalities, such as oral agents, need further study to validate their safety and efficacy. Finally, postpartum management of women with Gestational diabetes is critical because of their markedly increased risk of Type 2 diabetes in the future. Efforts should be made to prevent gestational diabetes in subsequent pregnancies. Because body fat and diet contribute to the risk of gestational diabetes mellitus, patients who lose weight before pregnancy and follow an appropriate diet may lower their risk of gestational diabetes mellitus.