Developing Clinical Decision Support System using Machine Learning Methods for Type 2 Diabetes Drug Management.

Background and Objectives: Application of artificial intelligence/machine learning (AI/ML) for automation of diabetes management can enhance equitable access to care and ensure delivery of minimum standards of care. Objective of the current study was to create a clinical decision support system using machine learning approach for diabetes drug management in people living with Type 2 diabetes. Methodology: Study was conducted at an Endocrinology clinic and data collected from the electronic clinic management system. 15485 diabetes prescriptions of 4974 patients were accessed. A data subset of 1671 diabetes prescriptions of 940 patients with information on diabetes drugs, demographics (age, gender, body mass index), biochemical parameters (HbA1c, fasting blood glucose, creatinine) and patient clinical parameters (diabetes duration, compliance to diet/exercise/medications, hypoglycemia, contraindication to any drug, summary of patient self monitoring of blood glucose data, diabetes complications) was used in analysis. An input of patient variables were used to predict all diabetes drug classes to be prescribed. Random forest algorithms were used to create decision trees for all diabetes drugs. Results and Conclusion: Accuracy for predicting use of each individual drug class varied from 85% to 99.4%. Multi-drug accuracy, indicating that all drug predictions in a prescription are correct, stands at 72%. Multi drug class accuracy in clinical application may be higher than this result, as in a lot of clinical scenarios, two or more diabetes drugs may be used interchangeably. This report presents a first positive step in developing a robust clinical decision support system to transform access and quality of diabetes care.

Vitamin B12 Deficiency is Endemic in Indian Population: A Perspective from North India.

BACKGROUND: Vitamin B12 deficiency is believed to be widespread in Indian population. However, more data is needed to fuel a meaningful debate on preventive and therapeutic strategies. AIMS AND OBJECTIVES: Objective of the current study is to evaluate status of vitamin B12 levels in people from a tier 3 city and among people living in an urban area with or without diabetes. SETTINGS AND DESIGN: Retrospective, cross-sectional study. METHODOLOGY: Data captured in electronic medical records (EMR) of an endocrine practice and from a diagnostic laboratory was analysed. STATISTICAL ANALYSIS USED: Statistical analysis was done using open source software "Jamovi". RESULTS: Prevalence of vitamin B12 deficiency (Vitamin B12 levels <200 pg/ml) in tier 3 city was 47.19% (n = 267). From an urban endocrine practice, database of 11913 patients was searched for reports of vitamin B12 levels. Prevalence of vitamin B12 deficiency was 37.76% in people with pre-diabetes (n = 92), 31.23% in people with endocrine problems other than diabetes and pre-diabetes (n = 285) and 18.25% in people with diabetes (n = 378). Tier 3 city population had significantly lower vitamin B12 levels than people living in an urban area and attending an endocrine clinic. Vitamin B12 levels were significantly higher in people with diabetes as compared to people with other endocrine problems. CONCLUSION: Prevalence of vitamin B12 deficiency is 47% in north Indian population. People with diabetes have higher vitamin B12 levels than general population though still have high prevalence of deficiency. This data shows that Vitamin B12 deficiency is widespread in Indian population.

Temporal Change in Profile of Association between Diabetes, Obesity, and Age of Onset in Urban India: A Brief Report and Review of Literature.

CONTEXT: Indian population is known to develop diabetes at lower body mass index (BMI) and younger age than their western counterparts. However, data on the prevalence of overweight/obesity in people with diabetes have not been well documented in the Indian literature. The current study also establishes time trends for the age of onset of diabetes mellitus type 2 in Urban India. AIMS: The aim of this study was to analyze the current trend in the presentation of people with diabetes in a metropolitan city (Urban residents) in context to age, sex, and BMI. SETTINGS AND DESIGN: Retrospective cross-sectional study. SUBJECTS AND METHODS: Data captured in clinic electronic medical records from May 2015 to March 2017 are analyzed. The profile of newly diagnosed diabetes is also presented. STATISTICAL ANALYSIS USED: Data were analyzed descriptively and for statistical analysis, SPSS software was used. RESULTS: A total of 1473 patients with age 52.6 ± 12.7 (mean ± standard deviation) and BMI of 27.86 ± 4.83 kg/m2 were analyzed. About 17.6% were overweight; 71.5% were obese. The significant higher proportion of females was overweight/obese (91.2% vs. 82% of males; P < 0.0001). Females had significantly higher mean BMI than males (29.19 ± 4.99 vs. 26.93 ± 4.83; P ≤ 0.001). Overall, mean HbA1c of patients was 7.84% ±1.61% with no significant difference between genders. However, there was no difference in mean HbA1c based on either gender or BMI category. Among people with new-onset diabetes, there was even higher proportion of people with BMI ≥23 kg/m2 (overall 89.1%, males 87.6%, and females 91.6%). About 43.8% of people presented at <40 years age. Totally, 49.5% of males presented at age <40 years as compared to 33.9% of females. CONCLUSIONS: The prevalence of obesity among people with diabetes in Indian setting is now as high as in western studies. Indians are now presenting with diabetes at even earlier age with higher BMI.

Local field potential decoding of the onset and intensity of acute pain in rats.

Pain is a complex sensory and affective experience. The current definition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These definitions can be subjective and do not take into consideration signals in the neural system. Local field potentials (LFPs) represent summed electrical currents from multiple neurons in a defined brain area. Although single neuronal spike activity has been shown to modulate the acute pain, it is not yet clear how ensemble activities in the form of LFPs can be used to decode the precise timing and intensity of pain. The anterior cingulate cortex (ACC) is known to play a role in the affective-aversive component of pain in human and animal studies. Few studies, however, have examined how neural activities in the ACC can be used to interpret or predict acute noxious inputs. Here, we recorded in vivo extracellular activity in the ACC from freely behaving rats after stimulus with non-noxious, low-intensity noxious, and high-intensity noxious stimuli, both in the absence and chronic pain. Using a supervised machine learning classifier with selected LFP features, we predicted the intensity and the onset of acute nociceptive signals with high degree of precision. These results suggest the potential to use LFPs to decode acute pain.

Differential processing of thalamic information via distinct striatal interneuron circuits.

Recent discoveries of striatal GABAergic interneurons require a new conceptualization of the organization of intrastriatal circuitry and their cortical and thalamic inputs. We investigated thalamic inputs to the two populations of striatal neuropeptide Y (NPY) interneurons, plateau low threshold spike (PLTS) and NPY-neurogliaform (NGF) cells. Optogenetic activation of parafascicular inputs evokes suprathreshold monosynaptic glutamatergic excitation in NGF interneurons and a disynaptic, nicotinic excitation through cholinergic interneurons. In contrast, the predominant response of PLTS interneurons is a disynaptic inhibition dependent on thalamic activation of striatal tyrosine hydroxylase interneurons (THINs). In contrast, THINs do not innervate NGF or fast spiking interneurons, showing significant specificity in THINs outputs. Chemospecific ablation of THINs impairs prepulse inhibition of the acoustic startle response suggesting an important behavioural role of this disynaptic pathway. Our findings demonstrate that the impact of the parafascicular nucleus on striatal activity and some related behaviour critically depend on synaptic interactions within interneuronal circuits.

Chronic pain induces generalized enhancement of aversion.

A hallmark feature of chronic pain is its ability to impact other sensory and affective experiences. It is notably associated with hypersensitivity at the site of tissue injury. It is less clear, however, if chronic pain can also induce a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs. Here, we showed that chronic pain in one limb in rats increased the aversive response to acute pain stimuli in the opposite limb, as assessed by conditioned place aversion. Interestingly, neural activities in the anterior cingulate cortex (ACC) correlated with noxious intensities, and optogenetic modulation of ACC neurons showed bidirectional control of the aversive response to acute pain. Chronic pain, however, altered acute pain intensity representation in the ACC to increase the aversive response to noxious stimuli at anatomically unrelated sites. Thus, chronic pain can disrupt cortical circuitry to enhance the aversive experience in a generalized anatomically nonspecific manner.