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IntroductionTuberculosis (TB) remains a significant health concern in India, and its complexity is exacerbated by the rising occurrence of non-communicable diseases such as diabetes mellitus (DM). Recognizing that DM is a risk factor for active TB, the emerging comorbidity of TB and PDM (TB-PDM) presents a particular challenge. Our study focused on the impact of PDM on cytokine and chemokine profiles in patients with pulmonary tuberculosis TB) who also have PDM.Materials and methodsWe measured and compared the cytokine (GM-CSF, IFN-γ, IL-1α/IL-1F1, IL-1β/IL-1F2, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70, IL-13, IL-17/IL-17A, IL-18/IL-1F4, TNF-α) and chemokine (CCL1, CCL2, CCL3, CCL4, CCL11, CXCL1, CXCL2, CXCL9, CXCL10, and CXCL11) levels in plasma samples of TB-PDM, only TB or only PDM using multiplex assay.ResultsWe observed that PDM was linked to higher mycobacterial loads in TB. Patients with coexisting TB and PDM showed elevated levels of various cytokines (including IFNγ, TNFα, IL-2, IL-17, IL-1α, IL-1β, IL-6, IL-12, IL-18, and GM-CSF) and chemokines (such as CCL1, CCL2, CCL3, CCL4, CCL11, CXCL1, CXCL9, CXCL10, and CXCL11). Additionally, cytokines such as IL-18 and GM-CSF, along with the chemokine CCL11, were closely linked to levels of glycated hemoglobin (HbA1c), hinting at an interaction between glycemic control and immune response in TB patients with PDM.ConclusionOur results highlight the complex interplay between metabolic disturbances, immune responses, and TB pathology in the context of PDM, particularly highlighting the impact of changes in HbA1c levels. This emphasizes the need for specialized approaches to manage and treat TB-PDM comorbidity.

Introduction Evidence on the prevalence of foot problems among people with diabetes in India at a national level is lacking. Hence, this study was aimed to assess the burden of high-risk (HR) feet in people with diabetes across India.Research design and methods A cross-sectional national-level project ‘Save the Feet and Keep Walking’ campaign was conducted by the Research Society for the Study of Diabetes in India (RSSDI) from July 10, 2022 to August 10, 2022. A modified version of 3 min foot examination was used to assess the foot problems. Around 10 000 doctors with RSSDI membership were trained online to conduct foot screening and provided a standardised monofilament for detection of loss of protective sensation. People with diabetes aged >18 years who visited the clinics during the study period were examined for foot problems. Data were collected online using the semi-structured questionnaire. A total of 33 259 participants with complete information were included for the final analysis. The foot at risk was categorised based on International Working Group on the Diabetic Foot guidelines 2023.Results Nearly 75% of the participants were aged above 45 years. Around 49% had diabetes duration >5 years and uncontrolled diabetes (hemoglobin A1c >8%). Presence of history of foot ulcer (20%), lower limb amputation (15.3%), foot deformities (24.5%) and absence of diminished dorsal pedis and posterior tibial pulses (26.4%) was noted in the study participants. Around 25.2% of them had HR feet and highly prevalent among males. Diabetic kidney and retinal complications were present in 70% and 75.5% of people with HR feet. Presence of heel fissures (OR (95% CI) 4.6 (4.2 to 5.1)) and callus or corns (OR (95% CI) 3.6 (3.3 to 4.0)) were significantly associated with HR feet.Conclusions One-fourth of people with diabetes were found to have HR feet in India. The findings are suggestive of regular screening of people with diabetes for foot problems and strengthening of primary healthcare.

Summary: Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

IntroductionThe assessment of tuberculosis (TB) treatment outcomes predominantly relies on sputum culture conversion status. To enhance treatment management, it is crucial to identify non-sputum-based biomarkers that can predict unfavorable outcomes. Cytokines are widely studied as diagnostic biomarkers for active TB. However, their potential as indicators for unfavorable treatment outcomes remains uncertain.MethodologyThis study was conducted within a well-characterized cohort comprising newly diagnosed patients with drug-sensitive pulmonary TB, confirmed through sputum smear and culture positivity. Our objective was to elucidate the TB antigen-stimulated cytokine profile at pre-treatment and at 2 months into anti-TB treatment (ATT) in patients with unfavorable treatment outcomes (cases, n = 27) in comparison to recurrence-free, microbiologically cured controls (n = 31). Whole blood was stimulated with TB antigens using the QuantiFERON In-tube gold method, and plasma supernatants were subjected to a panel of 14 cytokine measurements.ResultsIn our study, pre-treatment analysis revealed that eight cytokines (IL-2, IFN-γ, TNF-α, IL-6, IL-10, IL-17A, IL-18, and GM-CSF) were significantly elevated at baseline in cases compared to cured controls, both in unstimulated conditions and following TB antigen (CFP10, ESAT6, and TB7.7) stimulation. A similar pattern was observed at the 2-month mark of ATT, with eight cytokines (IL-2, IL-10, IL-13, IFN-γ, IL-6, IL-12p70, IL-17A, and TNF-α) showing significant differences between the groups. Importantly, no variations were detected following mitogen stimulation, underscoring that these distinctive immune responses are primarily driven by TB-specific antigens.ConclusionOur findings indicate that individuals with unfavorable TB treatment outcomes display a characteristic cytokine profile distinct from TB-cured patients, even before commencing ATT. Therefore, the levels of specific cytokine pre-treatment and at the 2-month point in the course of treatment may serve as predictive immune markers for identifying individuals at risk of unfavorable TB treatment outcomes, with these responses being predominantly influenced by TB-specific antigens.

Summary: Tuberculosis (TB) is one of the leading causes of death worldwide, and Diabetes Mellitus is one of the major comorbidities (TB/DM) associated with the disease. A total of 103 differentially expressed ncRNAs have been identified in the TB and TB/DM comparisons. A machine learning algorithm was employed to identify the most informative lncRNAs: ADM-DT, LINC02009, LINC02471, SOX2-OT, and GK-AS1. These lncRNAs presented substantial accuracy in classifying TB from HC (AUCs >0.85) and TB/DM from HC (AUCs >0.90) in the other three countries. Genes with significant correlations with the five lncRNAs enriched common pathways in Brazil and India for both TB and TB/DM. This suggests that lncRNAs play an important role in the regulation of genes related to the TB immune response.

Abstract Diabetes mellitus (DM) increases tuberculosis (TB) severity. We compared blood gene expression in adults with pulmonary TB, with or without diabetes mellitus (DM) from sites in Brazil and India. RNA sequencing (RNAseq) performed at baseline and during TB treatment. Publicly available baseline RNAseq data from South Africa and Romania reported by the TANDEM Consortium were also analyzed. Across the sites, differentially expressed genes varied for each condition (DM, TB, and TBDM) and no pattern classified any one group across all sites. A concise signature of TB disease was identified but this was expressed equally in TB and TBDM. Pathway enrichment analysis failed to distinguish TB from TBDM, although there was a trend for greater neutrophil and innate immune pathway activation in TBDM participants. Pathways associated with insulin resistance, metabolic dysfunction, diabetic complications, and chromosomal instability were positively correlated with glycohemoglobin. The immune response to pulmonary TB as reflected by whole blood gene expression is substantially similar with or without comorbid DM. Gene expression pathways associated with the microvascular and macrovascular complications of DM are upregulated during TB, supporting a syndemic interaction between these coprevalent diseases.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease’s progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)—based preventive, precision, and personalized (aiP3) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP3 model signifies a promising advancement in CVD/Stroke risk assessment.

The past two decades have witnessed telemedicine becoming a crucial part of health care as a method to facilitate doctor-patient interaction. Due to technological developments and the incremental acquisition of experience in its use, telemedicine’s advantages and cost-effectiveness has led to it being recognised as specifically relevant to diabetology. However, the pandemic created new challenges for healthcare systems and the rate of development of digital services started to grow exponentially. It was soon discovered that COVID-19-infected patients with diabetes had an increased risk of both mortality and debilitating sequelae. In addition, it was observed that this higher risk could be attenuated primarily by maintaining optimal control of the patient’s glucose metabolism. As opportunities for actual physical doctor-patient visits became restricted, telemedicine provided the most convenient opportunity to communicate with patients and maintain delivery of care. The wide range of experiences of health care provision during the pandemic has led to the development of several excellent strategies regarding the applicability of telemedicine across the whole spectrum of diabetes care. The continuation of these strategies is likely to benefit clinical practice even after the pandemic crisis is over.

Background: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. Objective: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.

IntroductionChitinase, Indoleamine 2,3-dioxygenesae-1 (IDO-1) and heme oxygenase-1 (HO-1) are candidate diagnostic biomarkers for tuberculosis (TB). Whether these immune markers could also serve as predictive biomarkers of unfavorable treatment outcomes in pulmonary TB (PTB) is not known.MethodsA cohort of newly diagnosed, sputum culture-positive adults with drug-sensitive PTB were recruited. Plasma chitinase protein, IDO protein and HO-1 levels measured before treatment initiation were compared between 68 cases with unfavorable outcomes (treatment failure, death, or recurrence) and 108 control individuals who had recurrence-free cure.ResultsPlasma chitinase and IDO protein levels but not HO-1 levels were lower in cases compared to controls. The low chitinase and IDO protein levels were associated with increased risk of unfavourable outcomes in unadjusted and adjusted analyses. Receiver operating characteristic analysis revealed that chitinase and IDO proteins exhibited high sensitivity and specificity in differentiating cases vs controls as well as in differentiating treatment failure vs controls and recurrence vs controls, respectively. Classification and regression trees (CART) were used to determine threshold values for these two immune markers.DiscussionOur study revealed a plasma chitinase and IDO protein signature that may be used as a tool for predicting adverse treatment outcomes in PTB.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Background: The first national-level multicentric clinic-based registry of youth-onset diabetes from India was started in the year 2006 by the Indian Council of Medical Research (ICMR). Objective: In this study, we present the data collected from one of the Regional Collaborating Centre, Chennai (RCC03) of the ICMR Young Diabetes Registry (YDR). Materials and Methods: YDR recruited young diabetes participants reporting on/after January 1, 2000, with age onset ≤25 years at the time of diagnosis of diabetes, and residing within the Chennai Metropolitan Area. The reporting centers (RCs) that were willing to participate in the registry were included, and their staff was trained to fill-in the baseline and follow-up proforma. Results: Overall, 29 RCs participated, which includes six government hospitals, and remaining are private speciality hospitals or single-physician clinics. So far, RCC03 had contributed 4194 young diabetes participants to ICMR-YDR from the Chennai region. Among the registered 48.1% (n = 2020) were type 1 diabetes mellitus (T1DM), 43.4% (n = 1821) were type 2 diabetes mellitus (T2DM), 6.4% (n = 269) were gestational diabetes mellitus, and remaining 2.0% (n = 84) had secondary diabetes. Among T1DM, 58% of them had age onset of

AimThe prevalence of latent tuberculosis infection (LTBI) among diabetes patients is poorly studied. In the present study, the prevalence of LTBI among pre-diabetes and diabetes patients was studied, along with immunoendocrine biomarkers (n = 804).MethodsLTBI was screened by Quantiferon TB gold in Normal glucose tolerance [(NGT); n = 170, [Pre-diabetes (PDM; n = 209), Newly diagnosed diabetes (NDM; n = 165) and Known diabetes (KDM; n = 260) subjects. CRP, TNF-α, IL-6, IL-1β, IFN-β, IL-12, IFN-γ, IL-2, insulin, leptin, and adiponectin levels in serum and IFN-γ levels in quantiferon supernatants were quantified by ELISA. The expression of T-bet was quantified using qRT-PCR. Serum TBARS and nitrite levels were quantified by colorimetry.ResultsThe LTBI prevalence was 32% in NGT, 23% in PDM, 24% in NDM, and 32% in KDM groups, with an adjusted OR of 0.61 (p < 0.05). Downregulation of CRP, TNF-α, and nitrites and upregulation of adiponectin could be responsible for LTBI mediated protection against insulin resistance (IR), while the high levels of IL-1β, IL-12, and leptin could be responsible for IR mediated anti-TB immunity. The defective antigen-specific IFN-γ response, as seen in the KDM group, could be responsible for the low detection rate of LTBI and high probability of endogenous reactivation.ConclusionThere appears to be a biphasic relationship between diabetes-latent tuberculosis: At the early stages of diabetes it is reciprocal, while at a late stage it is synergistic, this important phenomenon obviously needs further research.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AIbased platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high-moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image-based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

BackgroundType 2 diabetes mellitus (T2DM) worldwide continues to increase, in particular in India. Early T2DM diagnosis followed by appropriate management will result in more cardiovascular event free life years. However, knowledge of the cardiovascular profile of newly diagnosed T2DM patients is still limited. The aim of this study was to understand the extent of cardiovascular disease (CVD) risk of newly diagnosed T2DM patients in India.MethodsA cross sectional observational study was conducted to evaluate clinical laboratory and socio-demographic parameters of 5,080 newly diagnosed T2DM patients (48.3 ± 12.8 years of age; 36.7% female). In addition, we determined their cardiovascular risk according to the guidelines of the Lipid Association of India (LAI) and the criteria of the QRISK3 score.ResultsOf the newly T2DM diagnosed patients in India 2,007(39.5%) were classified as "High risk" and 3,073 (60.5%) were classified as "Very high risk" based on LAI criteria. On average, patients had 1.7 ± 0.9 major atherosclerotic cardiovascular disease (ASCVD) risk factors. Low HDL-C value was the most frequent major risk (2,823; 55.6%) followed by high age (2,502; 49.3%), hypertension (2,141; 42.1%), smoking/tobacco use (1,078; 21.2%) and chronic kidney disease stage 3b or higher (568; 11.2%). In addition, 4,192 (82.5%) patients appeared to have at least one cholesterol abnormality and, if the latest LAI recommendations are applied, 96.5% (4,902) presented with lipid values above recommended targets. Based on the QRISK3 calculation Indian diabetes patients had an average CVD risk of 15.3 ± 12.3%, (12.2 ± 10.1 vs. 17.1 ± 13.5 [pConclusionsNewly diagnosed Indian T2DM patients are at high ASCVD risk. Our data therefore support the notion that further extension of nationwide ASCVD risk identification programs and prevention strategies to reduce the occurrence of cardiovascular diseases are warranted.

Systemic inflammation is a characteristic feature of pulmonary tuberculosis (PTB). Whether systemic inflammation is associated with treatment failure in PTB is not known. Participants, who were newly diagnosed, sputum smear and culture positive individuals with drug-sensitive PTB, were treated with standard anti-tuberculosis treatment and classified as having treatment failure or microbiological cure. The plasma levels of acute phase proteins were assessed at baseline (pre-treatment). Baseline levels of C-reactive protein (CRP), alpha-2 macroglobulin (a2M), Haptoglobin and serum amyloid P (SAP) were significantly higher in treatment failure compared to cured individuals. ROC curve analysis demonstrated the utility of these individual markers in discriminating treatment failure from cure. Finally, combined ROC analysis revealed high sensitivity and specificity of 3 marker signatures comprising of CRP, a2M and SAP in distinguishing treatment failure from cured individuals with a sensitivity of 100%, specificity of 100% and area under the curve of 1. Therefore, acute phase proteins are very accurate baseline predictors of PTB treatment failure. If validated in larger cohorts, these markers hold promise for a rapid prognostic testing for adverse treatment outcomes in PTB.

Periodontitis is a microbially driven, host-mediated disease that leads to loss of periodontal attachment and resorption of bone. It is associated with the elevation of systemic inflammatory markers and with the presence of systemic comorbidities. Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the majority of patients have mild symptoms, others experience important complications that can lead to death. After the spread of the COVID-19 pandemic, several investigations demonstrating the possible relationship between periodontitis and COVID-19 have been reported. In addition, both periodontal disease and COVID-19 seem to provoke and/or impair several cardiometabolic complications such as cardiovascular disease, type 2 diabetes, metabolic syndrome, dyslipidemia, insulin resistance, obesity, non-alcoholic fatty liver disease, and neurological and neuropsychiatric complications. Therefore, due to the increasing number of investigations focusing on the periodontitis-COVID-19 relationship and considering the severe complications that such an association might cause, this review aims to summarize all existing emerging evidence regarding the link between the periodontitis-COVID-19 axis and consequent cardiometabolic impairments.

Chronic granulomatous disease (CGD) is an inherited neutrophil phagocytic function disorder leading to recurrent infections, granuloma formations, and rarely autoimmune diseases. We report a case of genetically proven autosomal recessive CGD in a child with a mutation in NCF2 gene resulting in p67phox defect presenting with foot abscess caused by Serratia marcescens and autoimmune dactylitis at the same time. The case emphasizes the importance to consider a primary immunodeficiency in patients with musculoskeletal manifestations who develop unusual or opportunistic infections. We also hope awareness among pediatricians may lead to increased recognition of the autoimmune manifestations of CGD.

Abstract Background Ligands of the receptor for advanced glycation end products (RAGE) are key signalling molecules in the innate immune system but their role in tuberculosis-diabetes comorbidity (TB-DM) has not been investigated. Methods We examined the systemic levels of soluble RAGE (sRAGE), advanced glycation end products (AGE), S100A12 and high mobility group box 1 (HMGB1) in participants with either TB-DM, TB, DM or healthy controls (HC). Results Systemic levels of AGE, sRAGE and S100A12 were significantly elevated in TB-DM and DM in comparison to TB and HC. During follow up, AGE, sRAGE and S100A12 remained significantly elevated in TB-DM compared to TB at 2nd month and 6th month of anti-TB treatment (ATT). RAGE ligands were increased in TB-DM individuals with bilateral and cavitary disease. sRAGE and S100A12 correlated with glycated hemoglobin levels. Within the TB-DM group, those with known diabetes (KDM) revealed significantly increased levels of AGE and sRAGE compared to newly diagnosed DM (NDM). KDM participants on metformin treatment exhibited significantly diminished levels of AGE and sRAGE in comparison to those on non-metformin regimens. Conclusions Our data demonstrate that RAGE ligand levels reflect disease severity and extent in TB-DM, distinguish KDM from NDM and are modulated by metformin therapy.

Abstract Background Economic dimensions of implementing quality improvement for diabetes care are understudied worldwide. We describe the economic evaluation protocol within a randomised controlled trial that tested a multi-component quality improvement (QI) strategy for individuals with poorly-controlled type 2 diabetes in South Asia. Methods/design This economic evaluation of the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) randomised trial involved 1146 people with poorly-controlled type 2 diabetes receiving care at 10 diverse diabetes clinics across India and Pakistan. The economic evaluation comprises both a within-trial cost-effectiveness analysis (mean 2.5 years follow up) and a microsimulation model-based cost-utility analysis (life-time horizon). Effectiveness measures include multiple risk factor control (achieving HbA1c

Objectives: To study the association of Tissue inhibitors of matrix metalloproteinases (TIMP) levels with tuberculosis-diabetes comorbidity (TB-DM) comorbidity at baseline and in response to anti-TB treatment (ATT). Methods: We examined the levels of TIMP-1, -2, -3 and -4 in pulmonary tuberculosis alone (TB) or TB-DM at baseline and after ATT. Results: TIMP-1, -3 and -4 were significantly increased in TB-DM compared to TB at baseline and after ATT. ATT resulted in a significant reduction in TIMP-2 and -3 levels and a significant increase in TIMP-1 in both TB and TB-DM. TIMP-1, -3 and -4 were also significantly increased in TB-DM individuals with bilateral, cavitary disease and also exhibited a positive relationship with bacterial burden in TB-DM and HbA1c in all TB individuals. Within the TB-DM group, those known to be diabetic before incident TB (KDM) exhibited higher levels of TIMP-1, -2, -3 and -4 at baseline and TIMP-2 at post-treatment compared to those newly diagnosed with DM (NDM). KDM individuals on metformin treatment exhibited lower levels of TIMP-1, -2 and -4 at baseline and of TIMP-4 at post-treatment. Conclusions: TIMP levels were elevated in TB-DM, associated with disease severity and bacterial burden, correlated with HbA1c levels and modulated by duration of DM and metformin treatment.

AimDiabetic foot ulcer (DFU) is a major concern in diabetes and its control requires in-depth molecular investigation. The present study aimed to screen the expression of microRNA-210 (miR-210) and its association in hypoxic pathway in DFU patients.MethodsThe study consists of 3 groups of circulation samples (50 in each group of: healthy volunteers, T2DM and T2DM with DFU) and 2 groups of tissue samples (10 in each group of: control and T2DM with DFU). Expression of miR-210 and hypoxia inducible factor-1 alpha (HIF-1α), and its responsive genes such as VEGF, TNF-α, IL-6, BCl2, Bax and Caspase 3 were analyzed by RT-PCR, Western blot and ELISA analyses.ResultsThe HIF-1α expression decreased in DFU patients with increased miR-210 expression in both circulation and tissue biopsies. The circulatory IL-6 and inflammatory gene TNF-α expression was increased in DFU compared to healthy controls and T2DM subjects. Further, we found there was no alteration in the angiogenic marker, VEGF expression. In comparison, anti-apoptotic BCl2 was decreased and Bax and Caspase 3 was increased in DFU tissues relative to control.ConclusionsThe study showed that there was an inverse relationship between miR-210 and HIF-1α expression in patients with DFU, indicating that miR-210 may regulate the expression of the hypoxic gene.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Abstract Background Matrix metalloproteinases (MMPs) are considered to be key mediators of tuberculosis (TB) pathology but their role in tuberculosis – diabetes comorbidity (TB-DM) is not well understood. Methods To study the association of MMP levels with severity and extent of disease as well as bacterial burden in TB-DM, we examined the systemic levels of MMP-1, − 2, − 3, − 7, − 8, − 9, − 10, − 12 and − 13 in individuals with TB-DM and compared them to those with TB alone (TB) or healthy controls (HC). Results Circulating levels of MMP-1, − 2, − 3, − 7, − 10 and − 12 were significantly higher in TB-DM compared to both TB and HC and MMP -13 levels were higher in comparison to HC alone. To understand the effect of standard anti-tuberculosis therapy (ATT) on these MMP levels in TB-DM, we measured the levels of MMPs at the end of treatment (post-treatment). Our findings indicate that ATT is associated with a significant reduction in the levels of MMP-1, − 2, − 3, − 8 and − 13 post-treatment. Moreover, the levels of MMP-1, − 2, − 3, − 9 and − 12 were significantly higher in TB-DM individuals with cavitary disease and/or bilateral disease at baseline but not post-treatment. Similarly, the levels of MMP -1, − 2, − 3 and − 8 exhibited a significant positive relationship with bacterial burden and HbA1c levels at baseline but not post-treatment. Within the TB-DM group, those known to be diabetic before incident TB (KDM) exhibited significantly higher levels of MMP-1, − 2, − 10 and − 12 at baseline and of MMP-1 and -3 post-treatment compared to those newly diagnosed with DM (NDM). Finally, KDM individuals on metformin treatment exhibited significantly lower levels of MMP-1, − 2, − 3, − 7, − 9 and − 12 at baseline and of MMP-7 post-treatment. Conclusions Our data demonstrate that systemic MMP levels reflect baseline disease severity and extent in TB-DM, differentiate KDM from NDM and are modulated by ATT and metformin therapy.

Abstract Comorbid diabetes mellitus (DM) increases tuberculosis (TB) risk and adverse outcomes but the pathological interactions between DM and TB remain incompletely understood. We performed an integrative analysis of whole blood gene expression and plasma analytes, comparing South Indian TB patients with and without DM to diabetic and non-diabetic controls without TB. Luminex assay of plasma cytokines and growth factors delineated a distinct biosignature in comorbid TBDM in this cohort. Transcriptional profiling revealed elements in common with published TB signatures from cohorts that excluded DM. Neutrophil count correlated with the molecular degree of perturbation, especially in TBDM patients. Body mass index and HDL cholesterol were negatively correlated with molecular degree of perturbation. Diabetic complication pathways including several pathways linked to epigenetic reprogramming were activated in TBDM above levels observed with DM alone. Our data provide a rationale for trials of host-directed therapies in TBDM, targeting neutrophilic inflammation and diabetic complication pathways to address the greater morbidity and mortality associated with this increasingly prevalent dual burden of communicable and non-communicable diseases.

BackgroundDiabetic nephropathy is a major complication of diabetes and an established risk factor for cardiovascular events. Lipid abnormalities occur in patients with diabetic nephropathy, which further increase their risk for cardiovascular events. We compared the degree of dyslipidemia among type 2 diabetes mellitus (T2DM) subjects with and without nephropathy and analyzed the factors associated with nephropathy among them.MethodsIn this retrospective study, T2DM patients with overt nephropathy were enrolled in the study group (n=89) and without nephropathy were enrolled in the control group (n=92). Both groups were matched for age and duration of diabetes. Data on total cholesterol (TC), triglycerides (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), urea and creatinine were collected from the case sheets. TG/HDL-C ratio, a surrogate marker for small, dense, LDL particles (sdLDL) and estimated glomerular filtration rate (eGFR) were calculated using equations. Multivariate analysis was done to determine the factors associated with eGFR.ResultsDyslipidemia was present among 56.52% of control subjects and 75.28% of nephropathy subjects (P=0.012). The percentage of subjects with atherogenic dyslipidemia (high TG+low HDL-C+sdLDL) was 14.13 among controls and 14.61 among nephropathy subjects. Though serum creatinine was not significantly different, mean eGFR value was significantly lower among nephropathy patients (P=0.002). Upon multivariate analysis, it was found that TC (P=0.007) and HDL-C (P=0.06) were associated with eGFR among our study subjects.ConclusionOur results show that dyslipidemia was highly prevalent among subjects with nephropathy. Regular screening for dyslipidemia may be beneficial in controlling the risk for adverse events among diabetic nephropathy patients.

Host eicosanoids are lipid mediators of inflammation that are commonly accepted as important modulators of the host immune response in Mycobacterium tuberculosis infection. During active tuberculosis (TB), eicosanoids may play an important role in the regulation of inflammatory responses. However, a detailed investigation of the relationship of eicosanoids in TB and TB-diabetes comorbidity (TB-DM) and association to disease pathology or bacterial burdens has not been studied. To study this, we examined the plasma levels of Lipoxin A4 (LXA4), 15-epi-LXA4, Leukotriene B4 (LTB4), and Prostaglandin E2 (PGE2) in individuals with either TB-DM, TB, diabetes mellitus (DM) or healthy controls (HC). Plasma levels of LXA4, 15-epi-LXA4, and PGE2 were significantly increased while the levels of LTB4 were significantly decreased in TB-DM and TB group compared to DM and HC. The ratio of LXA4 to LTB4 and 15-epiLXA4 to LTB4 was significantly enhanced in TB-DM compared to TB. Moreover, the levels of LXA4, 15-epi-LXA4 and the ratios of LXA4 to LTB4 and 15-epiLX4 to LTB4 were significantly increased in TB individuals with bilateral or cavitary disease and these markers also revealed a significant positive relationship with bacterial burden. At the completion of anti-tuberculosis therapy (ATT), levels of LXA4, 15-epi-LXA4, and PGE2 in TB-DM and TB groups were diminished and levels of LTB4 were enhanced in the TB group compared to pre-treatment. Our data imply that alteration and upregulation of eicosanoids are standard characteristics of TB-DM co-morbidity. Our data also demonstrate that modulation in the eicosanoid levels reflect disease severity and extent in TB and TB-DM and are modulated by ATT.

Diabetes mellitus (DM) increases risk for pulmonary tuberculosis (TB) and adverse treatment outcomes. Systemic hyper-inflammation is characteristic in people with TB and concurrent DM (TBDM) at baseline, but the impact of TB treatment on this pattern has not been determined. We measured 17 plasma cytokines and growth factors in longitudinal cohorts of Indian and Brazilian pulmonary TB patients with or without DM. Principal component analysis revealed virtually complete separation of TBDM from TB individuals in both cohorts at baseline, with hyper-inflammation in TBDM that continued through treatment completion at six months. By one year after treatment completion, there was substantial convergence of mediator levels between groups within the India cohort. Non-resolving systemic inflammation in TBDM comorbidity could reflect delayed lesion sterilization or non-resolving sterile inflammation. Either mechanism portends unfavorable long-term outcomes including risk for recurrent TB and for damaging immune pathology.

BACKGROUND:The association of antimicrobial peptides (AMPs) with tuberculosis-diabetes comorbidity (PTB-DM) is not well understood. METHODS:To study the association of AMPs with PTB-DM, we examined the systemic levels of cathelicidin (LL37), human beta defensin- 2 (HBD2), human neutrophil peptides 1-3, (HNP1-3) and granulysin in individuals with either PTB-DM, PTB, latent TB (LTB) or no TB infection (NTB). RESULTS:Circulating levels of cathelicidin and HBD2 were significantly higher and granulysin levels were significantly lower in PTB-DM compared to PTB, LTB or NTB, while the levels of HNP1-3 were significantly higher in PTB-DM compared to LTB or NTB individuals. Moreover, the levels of cathelicidin and/or HBD2 were significantly higher in PTB-DM or PTB individuals with bilateral and cavitary disease and also exhibited a significant positive relationship with bacterial burden. Cathelidin, HBD2 and HNP1-3 levels exhibited a positive relationship with HbA1c and/or fasting blood glucose levels. Finally, anti-tuberculosis therapy resulted in significantly diminished levels of cathelicidin, HBD2, granulysin and significantly enhanced levels of HNP1-3 and granulysin in PTB-DM and/or PTB individuals. CONCLUSION:Therefore, our data demonstrate that PTB-DM is associated with markedly enhanced levels of AMPs and diminished levels of granulysin.

Aims: This study was planned to assess the development of cardiovascular disease (CVD) events over an 11-year period and to identify the associated risk factors that could predict the onset of CVD among subjects with type 2 diabetes. Methods: Retrospective data of 249 patients (M:F 149:100) with type 2 diabetes, from a cohort of 7800 patients, attending a tertiary care center for diabetes from January 2000 to December 2011 were retrieved and analyzed for this study. Sociodemographic and habitual risk factors, baseline diabetes duration, HbA1c and time of onset of CVD and its risk factors were collected from case records. Person-years method was used to calculate incident rate of CVD. Binary logistic regression analyses were done to identify predictors associated with CVD and its risk factors. Results: Incidence of CVD among subjects with diabetes was 5.6 cases/1000 person-years. Nearly 60% developed hypertension and dyslipidemia or both during the 11-year period. The most common complication was neuropathy (14.4%). Smoking [OR (95%CI)] [9.26 (1.6–54.9)] (p = 0.014) and heavy alcohol consumption [8.7 (1.1–69.8)] (p = 0.04) were significantly associated with CVD. Higher BMI was significantly associated with hypertension and dyslipidemia [2.4 (1.3–4.3)] (p = 0.003). Conclusions: Smoking and heavy alcohol consumption were significantly associated with CVD, and increased BMI was significantly associated with hypertension and dyslipidemia among subjects with type 2 diabetes in this study population. These findings emphasize the need for early identification and modification of risk factors associated with CVD events in patients with diabetes.

Patients with complications of diabetes such as retinopathy, nephropathy, and cardiovascular complications have increased hospital stay with greater economic burden. Prevention of complications should be started before the onset of type 1 diabetes mellitus (T1DM) by working on risk factors and thereafter by intervention upon confirmatory diagnosis which can prevent further damage to β-cells. The actual risk of getting microvascular complications like microalbuminuria and retinopathy progression starts at glycated hemoglobin (HbA1c) level of 7%. As per the American Diabetes Association, a new pediatric glycemic control target of HbA1c 20 years as compared to patients

Type 1 diabetes mellitus (T1DM) has a wide presence in children and has a high mortality rates. The disease, if left unmanaged, poses various challenges to the patient and healthcare providers, including development of diabetic complications and thus decreasing the life expectancy of the affected child. The challenges of T1DM include awareness of the disease that is very poor among the general public and also in parents of T1DM children along with the health care professionals. The challenge of lack of awareness of T1DM can be met by increasing public awareness programs, conducting workshops for diabetes educators regarding T1DM in children, newsletters, CMEs, online courses, and by structured teaching modules for diabetes educators. Diagnosis of T1DM was a challenge a few decades ago but the situation has improved today with diagnostic tests and facilities, made available even in villages. Investigation facilities and infrastructure, however, are very poor at the primary care level, especially in rural areas. Insulin availability, acceptability, and affordability are also major problems, compounded by the various types of insulin that are available in the market with a varied price range. But effective use of insulin remains a matter of utmost importance.

The epidemic of type 2 diabetes and the recognition that achieving specific glycemic goals can substantially reduce morbidity have made the effective treatment of hyperglycemia a top priority. Despite compelling evidence that tight glycemic control is crucial for delaying disease progression, increased risk of hypoglycemia associated with such control underscore the complexity of diabetes management. In most cases, hypoglycemia results from an excess of insulin, either absolute or relative to the available glucose substrate and the factors perhaps exacerbating the risk are pharmacokinetic imperfections, behavioral, co-morbidities etc. Additionally, many patients remain undiagnosed, and many diagnosed patients are not treated appropriately. In this article, the challenges of hypoglycemia, confronting health care providers and their patients with diabetes, are discussed for making treatment decisions that will help minimize risk of hypoglycemia and eventually overcome formidable barriers to optimal diabetes management. Strategies to treat and minimize the frequency and severity of hypoglycemia without compromising on glycemic goals are also presented.

Hypertension occurs twice as commonly in diabetics than in comparable nondiabetics. Patients with both disorders have a markedly higher risk for premature microvascular and macrovascular complications. Aggressive control of blood pressure (BP) reduces both micro- and macrovascular complications. In diabetic hypertensives, angiotensin converting enzyme inhibitors (ACEIs) are the first line in management of hypertension, and can be replaced by angiotensin II receptor blockers (ARBs) if patients are intolerant of them. Recent studies suggest ARBs to be on par with ACEI in reducing both macro- and microvascular risks. Adding both these agents may have a beneficial effect on proteinuria, but no extra macrovascular risk reduction. Thiazides can also be used as first line drugs, but are better used along with ACEI/ARBs. Beta-blockers [especially if the patient has coronary artery disease] and calcium channel blockers are used as second line add-on drugs. Multidrug regimens are commonly needed in diabetic hypertensives. Achieving the target BP of

Elevated non-high density lipoprotein cholesterol (non-HDL-C) was the commonest lipid abnormality among T2DM patients with cardiovascular events (CV) events. Prevalence of elevated non-HDL-C was 21.6% among patients who were on statin therapy and with optimal low density lipoprotein-cholesterol (LDL-C) levels. Despite an optimal LDL-C level, 47% of the T2DM patients with CV events had elevated non-HDL-C.

BACKGROUND: Diabetes mellitus (DM) is recognised as an important risk factor to tuberculosis (TB). India has high TB burden, along with rising DM prevalence. There are inadequate data on prevalence of DM and pre-diabetes among TB cases in India. Aim was to determine diabetes prevalence among a cohort of TB cases registered under Revised National Tuberculosis Control Program in selected TB units in Tamil Nadu, India, and assess pattern of diabetes management amongst known cases. METHODS: 827 among the eligible patients (n = 904) underwent HbA1c and anthropometric measurements. OGTT was done for patients without previous history of DM and diagnosis was based on WHO criteria. Details of current treatment regimen of TB and DM and DM complications, if any, were recorded. A pretested questionnaire was used to collect information on sociodemographics, habitual risk factors, and type of TB. FINDINGS: DM prevalence was 25.3% (95% CI 22.6-28.5) and that of pre-diabetes 24.5% (95% CI 20.4-27.6). Risk factors associated with DM among TB patients were age (31-35, 36-40, 41-45, 46-50, >50 years vs 50 years vs