Diabetes Risk Factors Research Articles

Presence of diabetic risk factors within a sample of community members in Windhoek, Namibia

Background: Non-communicable diseases (NCDs) continue to pose a significant challenge to global healthcare systems. In African contexts, diabetes ranks among the top four NCDs. The disease often goes undetected because patients are commonly asymptomatic in the early stages. Screening, early diagnosis and patient education may prolong life and reduce costs associated with managing the disease.Aim: To identify risk factors for diabetes among community members in Windhoek.Setting: Data were gathered during a health-screening outreach programme in Otjomuise, a peri-urban settlement near Windhoek, Namibia.Methods: An observational analytical cross-sectional study design was used, measuring weight, height, abdominal girth, random blood glucose (RBG) levels, lipid counts, and blood pressure in 342 participants. Descriptive analysis determined the percentage of participants with abnormal measurements indicating diabetes risk.Results: Analysis showed that 77% of participants had multiple risk factors for diabetes. Specifically, 67% had elevated RBG levels, 58% had elevated blood pressure, 45% had elevated body mass index, and 26% had raised lipid counts.Conclusion: These findings align with existing literature, which shows a significant portion of community members have undetected risk factors that may predispose them to the development of diabetes. Early detection, targeted interventions and ongoing monitoring for individuals with risk factors can save lives and reduce costs. Larger-scale research is necessary to better quantify the prediabetic population across various African contexts.Contribution: This study highlights the urgent need for early diabetes detection and intervention strategies in diverse African communities.

Effects of Baccharis dracunculifolia DC on an Innovative Animal Model of Cardiometabolic Syndrome

Background/Objective: Cardiometabolic syndrome (CMS) is a complex clinical condition that encompasses metabolic dysregulation, cardiovascular disease, and diabetes risk factors. Worldwide, CMS is underdiagnosed, and its occurrence significantly increases cardiovascular morbimortality. Despite available pharmacological treatments, the approach is fragmented, and the associated clinical conditions are treated independently. This approach may be partially due to limited preclinical models to mimic the clinical conditions of CMS. Therefore, our study aims to present an innovative animal model of cardiometabolic syndrome and evaluate the effects of Baccharis dracunculifolia on the set of clinical alterations associated with the condition. Methods: Female Wistar rats were induced to develop diabetes, fed a cholesterol-enriched diet, and exposed to the smoke of 9 cigarettes/day for 6 weeks. During the last 2 weeks, the rats were treated with vehicle, B. dracunculifolia (30, 100, and 300 mg/kg), or a combination of simvastatin and insulin. At the end of the treatment, plasma lipid levels were measured, and the liver was analyzed histologically for hepatic lipid quantification and oxidative stress assessment. Results: Phytochemical analysis revealed seven phenolic acids and six flavonoids in the extract. B. dracunculifolia showed significant hepatoprotective effects, reducing AST and ALT levels and lowering both plasma and hepatic lipid levels. The extract also reversed hepatic steatosis and demonstrated antioxidant properties. Conclusions: These findings suggest that B. dracunculifolia may be a therapeutic option for the metabolic dysregulation present in CMS.

Interventions for Aboriginal and Torres Strait Islander people with type 2 diabetes that modify its management and cardiometabolic risk factors: asystematic review.

To review studies of interventions for reducing the impact of type 2 diabetes in Aboriginal and Torres Strait Islander people. The primary aim was to review and summarise the characteristics and findings of the interventions. The secondary aims were to assess their effects on diabetes and cardiometabolic risk factors, and the proportions of people with type 2 diabetes who achieved therapeutic targets with each intervention. We searched eight electronic databases for publications of studies including Aboriginal or Torres Strait Islander people aged 15 years or older with diagnoses of type 2 diabetes, describing one or more diabetes interventions, and published in English during 1 January 2000 - 31 December 2020. Reference lists in the assessed articles were checked for further relevant publications. MEDLINE (Ovid), Web of Science (Clarivate), the Cochrane Library, Global Health (EBSCO), Indigenous Collection and Indigenous Australia (Informit), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and the World Health Organization International Clinical Trials Registry Platform (WHO-ICTRP). The database searches yielded 1424 unique records; after screening by title and abstract, the full text of 55 potentially relevant articles were screened, of which seventeen met our eligibility criteria: eleven cohort studies (seven retrospective audits and four prospective studies), three randomised controlled trials, and three observational, non-randomised follow-up studies. Twelve publications reported site-based (Aboriginal or Torres Strait Islander health service or diabetes clinic) rather than individual-based diabetes interventions. Interventions with statistically significant effects on mean glycated haemoglobin (HbA1c) levels were laparoscopic adjustable gastric banding, a 5-day diabetes self-management camp, treatment of Strongyloides stercoralis infections, community-based health worker-led management, point-of-care testing, and self-management approaches. Few interventions for Aboriginal and Torres Strait Islander people with type 2 diabetes have been reported in peer-reviewed publications. Improving diabetes care services resulted in larger proportions of people achieving therapeutic HbA1c targets. Outcomes were better when Aboriginal and Torres Strait Islander communities were involved at all levels of an intervention. High quality studies of holistic, culturally safe and accessible interventions should be the focus of research.

EPA and DHA inhibit LDL-induced upregulation of human adipose tissue NLRP3 inflammasome/IL-1β pathway and its association with diabetes risk factors

Elevated numbers of atherogenic lipoproteins (apoB) predict the incidence of type 2 diabetes (T2D). We reported that this may be mediated via the activation of the NLRP3 inflammasome, as low-density lipoproteins (LDL) induce interleukin-1 beta (IL-1β) secretion from human white adipose tissue (WAT) and macrophages. However, mitigating nutritional approaches remained unknown. We tested whether omega-3 eicosapentaenoic and docosahexaenoic acids (EPA and DHA) treat LDL-induced upregulation of WAT IL-1β-secretion and its relation to T2D risk factors. Twelve-week intervention with EPA and DHA (2.7 g/day, Webber Naturals) abolished baseline group-differences in WAT IL-1β-secretion between subjects with high-apoB (N = 17) and low-apoB (N = 16) separated around median plasma apoB. Post-intervention LDL failed to trigger IL-1β-secretion and inhibited it in lipopolysaccharide-stimulated WAT. Omega-3 supplementation also improved β-cell function and postprandial fat metabolism in association with higher blood EPA and mostly DHA. It also blunted the association of WAT NLRP3 and IL1B expression and IL-1β-secretion with multiple cardiometabolic risk factors including adiposity. Ex vivo, EPA and DHA inhibited WAT IL-1β-secretion in a dose-dependent manner. In conclusion, EPA and DHA treat LDL-induced upregulation of WAT NLRP3 inflammasome/IL-1β pathway and related T2D risk factors. This may aid in the prevention of T2D and related morbidities in subjects with high-apoB.Clinical Trail Registration ClinicalTrials.gov (NCT04496154): Omega-3 to Reduce Diabetes Risk in Subjects with High Number of Particles That Carry “Bad Cholesterol” in the Blood – Full Text View - ClinicalTrials.gov.

Epigenomic biomarkers of cardiometabolic disease: How far are we from daily practice?

Determining whether someone has cardiometabolic disease (CMD), especially in the early stages, can be complicated. Risk stratification ordinarily depends on an extended process relying on medical history that typically considers blood pressure, cholesterol, smoking and diabetes status. Physicians have long relied on these key patient characteristics to assess CMD risk. However, these widely used clinical assessments are often identified later in life and by definition, in those individuals with progressed disease. This is partly because the onset of CMD naturally occurs in adulthood, however, the underlying processes can occur much earlier in life, even in the absence of obvious symptoms. For one thing, the pathways towards pathology may exist for years before symptom onset. Thus, among other things, there are opportunities to provide doctors with better insights into future disease prediction especially in younger adults with diabetes. The rapid rise in CMD together with the increased rates of obesity and diabetes in this population only emphasises the importance of predictive molecular biomarkers. One notable aspect is that traditional risk scores, such as those based on cholesterol measurements, are frequently found to be within normal ranges in younger populations. At the same time, given the significant overlap in risk factors for cardiovascular disease (CVD) and diabetes, the unmet clinical need is for early biomarkers of CMD that may help improve risk assessment in younger adults. This editorial highlights advances in the use of polygenic risk scores and emerging utility of genetic biomarkers to define intermediate CMD phenotypes discussing new classification criteria involving DNA methylation of genes to improve risk assessment. CMD is the number one cause of mortality and accounts for 31% of all global deaths. CMD is also multifactorial, comprising cardiovascular disease (CVD) and diabetes that have significant overlap in risk factors and disease biology. Diabetes is arguably the strongest risk factor for CVD development. Accounting for almost 90% of diabetes cases worldwide, type 2 diabetes (T2D) affects about 527 million people. The global economic burden is estimated at 1.3 trillion USD annually and is close to 1.8% of global GDP [1]. Despite the progress in preventive and therapeutic measures of CVD, the increasing CMD rates only underscore the important need of molecular biomarkers for early detection [2]. Determining whether someone has CMD usually involves an extended diagnostic process that has become essential for risk stratification and disease prevention [3]. While the onset of CMD typically occurs in adulthood, disease development commences much earlier, and this has scientists questioning whether molecular biomarkers could improve current prognostic risk scores. Predicting which people with T2D are most likely to develop CVD remains a significant challenge despite the recent advances in genetic mapping.Graphical abstract

Inhaling Poor Health: The Impact of Air Pollution on Cardiovascular Kidney Metabolic Syndrome.

Air pollution, mostly from fossil fuel sources, is the leading environmental cause of global morbidity and mortality and is intricately linked to climate change. There is emerging evidence indicating that air pollution imposes most of its risk through proximate cardiovascular kidney and metabolic (CKM) etiologies. Indeed, there is compelling evidence linking air pollution to the genesis of insulin resistance, type 2 diabetes, hypertension, and other risk factors. Air pollution frequently coexists with factors such as noise, with levels and risks influenced substantially by additional factors such as social determinants and natural and built environment features. Persistent disparities regarding the impact and new sources of air pollution, such as wildfires attributable to climate change, have renewed the urgency to better understand root sources, characterize their health effects, and disseminate this information for personal protection and policy impacts. In this review, we summarize evidence associating air pollution with cardiovascular health, the impact of air pollution on CKM health, and how interactions with other exposures and personal characteristics may modify these associations. Finally, we discuss new integrated approaches to capture risk from air pollution in the context of an exposomic framework.

Postpartum dysglycaemia after early gestational diabetes: Follow-up of women in the TOBOGM randomised controlled trial

AimTo evaluate the incidence and predictors of postpartum dysglycaemia among high-risk women who develop early gestational diabetes (eGDM) prior to 20 weeks’ gestation. MethodsThis is a sub-study of the Treatment of Booking Gestational Diabetes (TOBOGM) Study, a randomised controlled trial of early or deferred treatment for women with risk factors for diabetes diagnosed with eGDM, using current WHO criteria. Overt diabetes in pregnancy was excluded. A repeat oral glucose tolerance test (oGTT) was recommended at 6–12 weeks postpartum. ResultsOf 793 participants, 352 (44.4%) underwent a postpartum oGTT. Baseline characteristics of participants with and without an oGTT were similar. Ninety-two (26.1%) had postpartum dysglycaemia: 11 (3.1%) diabetes, 31 (8.8%) impaired fasting glucose (IFG), 39 (11.1%) impaired glucose tolerance (IGT), and 11 (3.1%) combined IFG/IGT. Participants with postpartum dysglycaemia were more likely to have had past GDM, lower body mass index, more gestational weight gain, and higher 1 and 2-hour glucose concentrations on the early pregnancy oGTT. On logistic regression, higher 1 and 2-hour glucose concentration, previous GDM and greater gestational weight gain were independently associated with postpartum dysglycaemia. ConclusionThere is a high incidence of postpartum dysglycaemia among high-risk women with eGDM.

Challenging the American College of Obstetricians and Gynecologists' Clinical Practice Update on Screening for Pre-existing Diabetes and Early Gestational Diabetes.

A recent American College of Obstetricians and Gynecologists Clinical Practice Update continues to recommend targeted (as opposed to universal) screening for pregestational diabetes, no longer recommends screening for early gestational diabetes mellitus (GDM), and provides updated guidelines for immediate postpartum testing for diabetes in patients with GDM. Here, we present data that the targeted screening paradigm, which has repeatedly been shown to fail in practice because of its complexity, no longer makes sense in the context of the high and rising prevalence of diabetes and diabetic risk factors, and we argue that the time has come for universal early pregnancy screening for pregestational diabetes. Furthermore, the recommendation against early screening for GDM is based on 2021 U.S. Preventive Services Task Force guidance, which in turn is based almost entirely on a single underpowered study that excluded individuals at highest risk and does not account for more recent research showing benefits of early diagnosis and treatment. Universal early pregnancy screening for pregestational diabetes may also help to identify patients at risk who will benefit from early GDM diagnosis and treatment and may provide rationale for prioritizing postpartum diabetes testing.

Prevalence and risk factors for gestational diabetes in Jambi, Indonesia

The most common time for gestational diabetes mellitus (GDM), which affects anywhere from 1% to 40% of pregnant women, is between the 24th and 28th week of pregnancy. One of the provinces in Indonesia, Jambi, was the focus of this study, which aimed to characterize the incidence and risk factors of GDM. One hundred twenty-two women without GDM and twelve women with GDM participated in this case control research. Oral glucose tolerance tests were used to diagnose gestational diabetes mellitus (GDM) in women who were assessed between 24 and 28 weeks of gestation. Data Collection in this research used questionnaire about woman’s social demographics, history GDM, parity, family support, and social support. Multivariate analysis is used to measure the relationship between variables. The results of the study showed a huigh prevalence of GDM of 37,6%. The identified risk factors were hypertension (OR=2,121;CI95%=1,405-3,203), family history of DM (OR=1,610;CI95%=1,333-1,946),BMI>30 kg/m2(OR=1,465;CI95%=1,243-1,727) and history of GDM previous pregnancy (OR=2,857;CI95%=0,839-9,727). We conclude that a GDM risk factor score is required so that health services can utilize these indicators as gold standards for screening GDM, due to the large heterogeneity in GDM risk factors.

CCN5/WISP2 serum levels in patients with coronary artery disease and type 2 diabetes and its correlation with inflammation and insulin resistance; a machine learning approach

IntroductionStudies have shown various effects of CCN5/WISP2 on metabolic pathways, yet no prior investigation has established a link between its serum levels and CAD and/or T2DM. Therefore, this study seeks to explore the relation between CCN5 and the risk factor of CAD and/or diabetes, in comparison to individuals with good health, marking a pioneering endeavor in this field. MethodsThis case-control study investigates serum levels of CCN5, TNF-α, IL-6, adiponectin, and fasting insulin in a population of 160 individuals recruited into four equal groups (T2DM, CAD, CAD-T2DM, and healthy controls). Statistical tests comprise Chi-square tests, ANOVA, Spearman correlation, and logistic regression. ROC curves were used to represent the diagnostic potential of CCN5. Disease states are predicted by machine learning algorithms: Decision Tree, Gradient Boosted Trees, Random Forest, Naïve Bayes, and KNN. These models' performance was evaluated by various metrics, all of which were ensured to be robust by applying 10-fold cross-validation. Analyses were done in SPSS and GraphPad Prism and RapidMiner software. ResultsThe CAD, T2DM, and CAD-T2DM groups had significantly higher CCN5 concentrations compared to the healthy control group (CAD: 336.87 ± 107.36 ng/mL, T2DM: 367.46 ± 102.15 ng/mL, CAD-T2DM: 404.68 ± 108.15 ng/mL, control: 205.62 ± 63.34 ng/mL; P < 0.001). A positive and significant correlation was observed between CCN5 and cytokines (IL-6 and TNF-α) in all patient groups (P < 0.05). Multinomial logistic regression analysis indicated a significant association between CCN5 and T2DM-CAD, T2DM, and CAD conditions (P < 0.001) even after adjusting for gender, BMI, and age (P < 0.001). Regarding the machine learning models, the Naïve Bayes model showed the best performance for classifying cases of T2DM, achieving an AUC value of 0.938±0.066. For predicting CAD, the Random Forest classifier achieved the highest AUC value of 0.994±0.020. In the case of CAD-T2DM prediction, the Naïve Bayes model demonstrated the highest AUC of 0.981±0.059, along with an Accuracy of 97.50 % ± 7.91 % and an F-measure of 96.67 % ± 10.54 %. ConclusionOur study has revealed, for the first time, a positive connection between CCN5 serum levels and the risk of developing T2DM and CAD. Nonetheless, more research is needed to ascertain whether CCN5 can serve as a predictive marker.

Icosapent ethyl by baseline small dense low-density lipoprotein cholesterol: an analysis of REDUCE-IT

Abstract Background Small dense low-density lipoprotein cholesterol (sdLDL-C) is associated with cardiovascular (CV) risk. Purpose We assessed if baseline sdLDL-C modified the effect of icosapent ethyl among patients in REDUCE-IT. Methods REDUCE-IT randomized patients to icosapent ethyl vs placebo. In this post hoc analysis, patients ≥45 years with CV disease or ≥50 years with diabetes and CV risk factors were included. Patients were required to have triglycerides of 135-499 mg/dL and low-density lipoprotein cholesterol (LDL-C) of 41-100 mg/dL. Exclusion criteria included severe heart failure, acute severe liver disease, or hemoglobin A1c &amp;gt;10%. Patients were stratified by baseline calculated sdLDL-C. A threshold of 46 mg/dL was chosen because sdLDL-C greater than this cutoff has predicted CV risk despite well-controlled LDL-C. The primary outcome included a composite of CV death, nonfatal myocardial infarction (MI), nonfatal stroke, coronary revascularization, or unstable angina. The key secondary composite outcome included CV death, nonfatal MI, or nonfatal stroke. Outcomes were assessed with Cox proportional hazard models and interaction analyses were conducted to assess heterogeneity in treatment effect by baseline sdLDL-C. Results REDUCE-IT included 8179 patients, with 8157 (99.7%) patients having sdLDL-C data. Among these patients, 7698 (94.4%) had sdLDL-C &amp;lt;46 mg/dL and 459 (5.6%) had sdLDL-C ≥46 mg/dL. Median sdLDL-C was 34.2 mg/dL (interquartile range [IQR]: 30.3, 38.4 mg/dL) in the lower sdLDL-C group and 48.4 mg/dL (IQR: 47.1, 50.6 mg/dL) in the higher sdLDL-C group (Table). The placebo group had a greater event rate in the higher sdLDL-C group compared to the lower sdLDL-C group (72.0 vs 56.4 events per 1000 p-y), though event rates in the icosapent ethyl group were similar by sdLDL-C group (44.1 vs 43.3 events per 1000 p-y, respectively). Icosapent ethyl reduced the rate of the primary outcome compared with placebo (17.2% vs 22.0%; hazard ratio [HR]: 0.75 [95% CI: 0.68, 0.83]; P&amp;lt;0.0001). The number needed to treat [NNT] to avoid one primary endpoint event was 21. In the lower sdLDL-C group, icosapent ethyl decreased rates of the primary outcome (43.3 events per 1000 patient-years [p-y]) compared with the placebo group (56.4 events per 1000 p-y) (17.3% vs 21.7%; HR: 0.77 [95% CI: 0.69, 0.85]; NNT: 22). Similarly, in the higher sdLDL-C group, icosapent ethyl decreased the rate of the primary outcome (44.1 events per 1000 p-y) compared with the placebo group (72.0 events per 1000 p-y) (16.6% vs 26.4%; HR: 0.58 [95% CI: 0.38, 0.88]; NNT: 10). There was no significant interaction between treatment benefit and baseline sdLDL-C for the primary (Pinteraction = 0.22) and key secondary outcome (Pinteraction = 0.82). Findings were overall similar for the other secondary outcomes (Figure). Conclusion Icosapent ethyl reduced CV outcomes in patients with high CV risk and elevated triglycerides irrespective of low or high sdLDL-C.

Multi-modality deep learning model for prediction of chronic obstructive coronary artery disease

Abstract Background/Introduction The incidence of coronary artery disease (CAD) is on the rise with an increasing burden of stable ischemic heart disease (SIHD) and high prevalence of obesity, diabetes, hyperlipidemia, and other cardiovascular risk factors. Pre-test probability estimation for CAD based on demographic features and characterization of chest pain is insufficient to accurately assess whether further evaluation for CAD is warranted. Purpose To develop and validate a deep learning (DL) algorithm utilizing electrocardiogram (ECG) waveforms and clinical features to predict obstructive CAD. Methods Clinical data from subjects undergoing invasive angiography over a 4-year period at a quaternary care center exclusive of subjects who had concern for acute coronary syndromes were used for model training. ECGs obtained up to 5 years prior to angiography were included; ECGs were partitioned 8:1:1 for training, validation, and test datasets. Obstructive CAD was defined as receiving percutaneous coronary intervention during elective angiography. Convolutional neural network models were developed for ECG waveforms alone (DL-ECG), clinical features from standard risk scores (DL-Clinical), and the combination of ECG waveforms and clinical features (DL-ECG+Clinical); a commonly used pre-test probability estimation tool derived from the CAD Consortium study was used as a comparison (PTP). The models were evaluated based on area under the curve receiver operating characteristics (AUC). Shapley additive explanations (SHAP) analysis was performed to understand feature importance in the DL models. Results A total of 40,602 ECGs and 8,361 coronary angiograms from 7,201 patients were included in the analysis. The median age was 66 (IQR 56-75), 71.9% were men, 34.9% had hypertension, 30.6% had diabetes mellitus, and 39.0% had dyslipidemia. The PTP model (AUC 0.733 [0.717-0.750]) had similar performance as the DL-Clinical model (AUC 0.762 [0.746-0.778]). The DL-ECG model (AUC 0.741 [0.726-0.758]) had similar performance as both the clinical feature models. The DL-ECG+Clinical model (AUC 0.807 [0.793-0.822]) trained on ECG waveforms and clinical features had a superior performance (Figure 1). SHAP analysis for the DL-ECG+Clinical model revealed that ECG waveforms had the largest influence on the model’s output followed by age and chest pain type (Figure 2). Conclusions Multi-modality DL model utilizing ECG and commonly used features can improve prediction of obstructive CAD in SIHD compared to traditional pre-test probability estimates. Prospective research is warranted to determine whether incorporation of DL methods to ECG analysis effectively improves diagnosis and outcomes in obstructive CAD.Figure 1.Figure 2.

Lipids as the link between central obesity and diabetes: perspectives from mediation analysis

BackgroundCentral obesity is a well-recognized risk factor for diabetes, yet the potential role of lipids in the diabetes risk associated with central obesity remains unclear. This study aimed to explore the possible mediating role of 11 lipid parameters [high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), non-high-density lipoprotein cholesterol (Non-HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), remnant cholesterol (RC), and ratios of Non-HDL-C/HDL-C, RC/HDL-C, LDL/HDL-C, TG/HDL-C, TC/HDL-C] in the association of central obesity with diabetes risk.MethodsWe utilized data from 15,453 participants in the NAGALA longitudinal cohort to assess the association of baseline central obesity indicators [waist-height ratio (WHtR), waist circumference (WC)] and the 11 lipid parameters with diabetes risk. Mediation analysis models were constructed to explore the mediating role of lipid parameters in the association of WC/WHtR with diabetes.ResultsConfirmatory associative analysis using multivariable Cox regression showed that, except for Non-HDL-C, TC and LD-C, the remaining eight lipid parameters were significantly associated with WC/WHtR and diabetes risk. Mediation analysis indicated that TG, RC, HDL-C, and lipid ratios such as Non-HDL-C/HDL-C ratio, RC/HDL-C ratio, TG/HDL-C ratio, TC/HDL-C ratio and LDL/HDL-C ratio are potential lipids affecting the diabetes risk related to central obesity. Among these, the RC/HDL-C ratio seemed to contribute the most in the WC/WHtR-related diabetes risk association, with a mediation percentage of about 37%. Additionally, lipid ratio parameters appeared to play a more mediating role in the association of central obesity-related diabetes risk than individual lipids.ConclusionsIn central obesity-related diabetes risk, most lipids, especially lipid ratio parameters, play a significant mediating role. Given these findings, we advocate for increased efforts in multifactorial risk monitoring and joint management of diabetes. The evaluation of lipids, particularly lipid ratio parameters, may be holds substantial value in the prevention and management of diabetes risk under close monitoring of central obesity.

Gut microbiota predict retinopathy in patients with diabetes: A longitudinal cohort study

The gut microbiota has emerged as an independent risk factor for diabetes and its complications. This research aimed to delve into the intricate relationship between the gut microbiome and diabetic retinopathy (DR) through a dual approach of cross-sectional and prospective cohort studies. In our cross-sectional study cross-sectional investigation involving ninety-nine individuals with diabetes, distinct microbial signatures associated with DR were identified. Specifically, gut microbiome profiling revealed decreased levels of Butyricicoccus and Ruminococcus torques group, alongside upregulated methanogenesis pathways among DR patients. These individuals concurrently exhibited lower concentrations of short-chain fatty acids in their plasma. Leveraging machine learning models, including random forest classifiers, we constructed a panel of microbial genera and genes that robustly differentiated DR cases. Importantly, these genera also demonstrated significant correlations with dietary patterns and the molecular profiles of peripheral blood mononuclear cells. Building upon these findings, our prospective cohort study followed 62 diabetes patients over a 2-year period to assess the predictive value of these microbial markers. The results underlined the panel’s efficacy in predicting DR incidence. By stratifying patients based on the predictive genera and metabolites identified in the cross-sectional phase, we established significant associations between reduced levels of Butyricicoccus, plasma acetate, and increased susceptibility to DR. This investigation not only deepens our understanding of how gut microbiota influences DR but also underscores the potential of microbial markers as early indicators of disease risk. These insights hold promise for developing targeted interventions aimed at mitigating the impact of diabetic complications.Key points• Microbial signatures are differed in diabetic patients with and without retinopathy• DR-related taxa are linked to dietary habits and transcriptomic profiles• Lower abundances of Butyricicoccus and acetate were prospectively associated with DR

Association of risk factors on mortality in ST-elevation myocardial infarction patients with diabetes mellitus: a systematic review and meta-analysis of cohort studies

Abstract Introduction Despite advancements in medical therapy and revascularization strategies for ST-elevation myocardial infarction (STEMI), diabetes mellitus (DM) remains independently associated with enhanced morbidity and mortality in STEMI. The causes for this are still unknown. Therefore, we aimed to summarise and quantify existing evidence in a systematic review and meta-analysis on the association between risk factors and mortality in STEMI patients with DM from prospective and retrospective cohort studies. Methods The systematic literature search was performed on the database of PubMed, Scopus and the Cochrane library up to July 2022. Relevant data were extracted and the risk of bias (RoB) of each study was assessed by using the Quality In Prognosis Studies (QUIPS) tool. All steps were conducted by two researchers independently. Summarised relative risks (SRR) and 95% confidence interval (CI) were estimated using a random effects model. I² was calculated as a measure of inconsistency. Results Out of 7601 studies, 18 were included after full-text screening, meeting our inclusion criteria. The RoB was moderate for 6 and high for 12 studies. Results of 7 studies reported on in-hospital mortality, 7 on all-cause-mortality up to 72 months, 3 on both outcomes, and 1 on cardiac death followed-up for 3.5 years. We found an increased risk of mortality for female compared to male sex (SRR [95% CI]: 1.56 [1.20; 2.01], n = 3 studies), age per 5 year increase (SRR [95% CI]: 1.26 [1.23; 1.30], n = 10 studies), admission blood glucose ≥ 7,8 mmol/l (SRR [95% CI]: 2.54 [1.60; 4.04], n = 3 studies), insulin treatment (SRR [95% CI]: 1.72 [1.33; 2.21], n = 2 studies), and dedicated Killip classification III-IV (SRR [95% CI]: 2.97 [2.58; 3.41], n = 3 studies) in STEMI patients with DM. Similar findings were observed for pre-existing comorbidities: prior myocardial infarction (SRR [95% CI]: 1.25 [1.06; 1.47], n = 3 studies), and heart failure (SRR [95% CI]: 2.68 [1.83; 3.93], n = 2 studies). Current smoking (SRR [95% CI]: 0.68 [0.52; 0.88], n = 3 studies) and hypercholesterinemia (SRR [95% CI]: 0.67 [0.57; 0.79], n = 2 studies) showed a reduced risk for mortality, while no association was found for multivessel disease (SRR [95% CI]: 1.03 [0.55; 1.92], n = 3 studies). Conclusion We identified specific risk factors of diabetes for mortality after STEMI. Our results confirm higher risk of mortality among STEMI patients with DM, particularly in women, older individuals, those with high admission blood glucose or insulin-dependent diabetes, and cardiac comorbidities. The role of smoking and hypercholesterinemia as a predictor for mortality might be explained by reverse causality and warrants further investigation.

Relationship of Plasma Apolipoprotein C-I Truncation With Risk of Diabetes in the Multi-Ethnic Study of Atherosclerosis and the Actos Now for the Prevention of Diabetes Study.

Higher truncated-to-native apolipoprotein (apo) C-I proteoform ratios (C-I'/C-I) are associated with favorable cardiometabolic risk profiles, but their relationship with longitudinal changes in insulin resistance (IR) and incident diabetes is unknown. Plasma apoC-I proteoforms were measured by mass spectrometry immunoassay at baseline in 4,742 nondiabetic participants in the Multi-Ethnic Study of Atherosclerosis (MESA) and 524 participants with prediabetes in the Actos Now for Prevention of Diabetes (ACT NOW) study. The primary outcome was incident diabetes (fasting glucose [FG] ≥7.0 mmol/L or hypoglycemic medication use in MESA; FG ≥7.0 mmol/L or 2-h glucose ≥11.1 mmol/L in an oral glucose tolerance test [OGTT] in ACT NOW). Secondary outcomes were changes in FG and HOMA-IR in MESA, and OGTT-glucose area under the curve (AUCglucose) and Matsuda insulin sensitivity index (ISI) in ACT NOW. In MESA, a higher C-I'/C-I was associated with lower risk of diabetes (n = 564 events; HR 0.87 [95% CI 0.79, 0.95] per SD; P = 0.0036; median follow-up, 9 years), and smaller increases (follow-up adjusted for baseline) in FG (-0.5%; P < 0.0001) and HOMA-IR (-2.9%; P = 0.011) after adjusting for baseline clinical and demographic covariates, including plasma triglycerides and HDL cholesterol. Total apoC-I concentrations were not associated with changes in FG, HOMA-IR, or incident diabetes. In ACT NOW, higher C-I'/C-I was associated with smaller increases in AUCglucose (-1.8%; P = 0.0052), greater increases in ISI (7.2%; P = 0.0095), and lower risk of diabetes (n = 59 events; 0.66 [95% CI 0.48, 0.91]; P = 0.004; median follow-up, 2.5 years) after adjusting for treatment group and diabetes risk factors, including plasma lipids. Our results indicate that apoC-I truncation may contribute to changes in glucose levels, IR, and risk of diabetes.

Early pregnancy hyperglycaemia among pregnant women with risk factors for gestational diabetes increases the risk of pregnancy complications

The current diagnostic criteria for gestational diabetes (GDM) were based on data from women after 24 weeks of gestation, but this may not be equally applicable for earlier gestation. There is insufficient data regarding early pregnancy glycaemia and the associated complications. We retrospectively reviewed 39,483 deliveries and 12,918 with risk factors for GDM underwent oral glucose tolerance test (OGTT) before 24 weeks of gestation. A strong and progressively positive association was observed with any pregnancy complications in both fasting glucose (FG) and 2 h glucose (2hG). The increased risk of developing any pregnancy complications started from FG 4.5–4.7 mmol/L and 2hG of 6.2–6.9mmol/L. Every increase by 1 mmol/L in FG or 2 hG levels increased the risk of developing any complications (aOR 1.614 for FG and 1.131 for 2hG), pre-eclampsia (aOR 1.472 for FG and 1.143 for 2hG), maternal insulin use (aOR 12.821 for FG and 2.366 for 2hG), primary Caesarean section(aOR 1.274 for FG and 1.099 for 2hG), shoulder dystocia (aOR 1.941 for FG and 1.282 for 2hG), macrosomia(aOR 2.203 for FG and 1.072 for 2hG), and large for gestation age(aOR 2.157 for FG and 1.074 for 2hG). Therefore, glycaemic levels in early pregnancy among high-risk women positively associated with pregnancy complications, even at levels below the current recommended diagnostic criteria for GDM.

Synergistic amelioration of obesity in high‐fat diet–fed mice by Bifidobacterium longum and green banana powder through targeting multiple pathways

AbstractLong‐term consumption of high‐fat diet (HFD) is a major significant risk factor for various diseases, including hyperlipidemia, diabetes, cardiovascular disease, and certain cancers. Recently, there has been a focus on effective dietary methods to improve lipid absorption and accumulation in the body. Simultaneously, exploring non‐pharmacological interventions to counteract the adverse consequences of obesity is crucial. Probiotics and prebiotics emerge as promising alternatives in this context, with their combined utilization yielding substantial effects surpassing those achieved by individual application alone. The aim of this study was to investigate the combined effects of Bifidobacterium longum (BL) SYP‐B4138 and green banana powder (GBP) on HFD‐induced obesity while exploring their underlying mechanisms. The findings demonstrated that the combined intervention exhibited significantly greater efficacy than BL or GBP alone, including in mitigating weight gain and organ weight among obese mice, while also improving lipid profile, glucose tolerance, and inflammation levels. Furthermore, it effectively reduced liver fat accumulation. The mechanism study revealed that the combined intervention increased the expression of gene‐related lipid metabolism, such as Pparα, Cpt1a, Fxr, Cyp7a1, and Bsep, while decreasing expression of the regulatory genes involved in fat synthesis, Acc1, Srebpf1, and Pparγ. The correlation analysis of gut microbes and metabolites suggests that the combined effect of BL and GBP can reverse the intestinal microecological imbalance, leading to changes in bile acids, glycerophospholipids, and other related metabolites. Overall, this study demonstrates that BL combined with GBP can effectively alleviate dietary obesity, highlighting its potential as a non‐pharmacological intervention for weight reduction.

The association between blood glucose levels and lipids in the general adult population: results from NHANES (2005–2016)

ObjectiveAlthough abnormal lipid metabolism is one of the major risk factors for diabetes, the correlation between lipids and glucose is rarely discussed in the general population. The differences in lipid-glucose correlations across gender and ethnicity have been even more rarely studied. We examined the association between fasting blood glucose (FBG) and lipids, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (ApoB), using 6,093 participants aged 20 years or older from the National Health and Nutrition Examination Survey (NHANES).MethodsAnalyses were performed using multiple logistic regression and generalised additive models.ResultsWhen other confounders were considered, we found that fasting glucose was positively correlated with triglycerides and negatively correlated with HDL-C, whereas total cholesterol, LDL-C cholesterol, and fasting glucose were related to each other in a U-curve fashion, with inflection points of 5.17 mmol/L and 2.3 mmol/L, respectively.This relationship persisted in subgroups of different sexes and races. A positive correlation was found between fasting glucose and ApoB, but subgroup analyses revealed that this relationship was not correlated across gender and race.ConclusionIn the general population, fasting blood glucose levels were positively correlated with TG, negatively correlated with HDL-C, and U-shaped with total cholesterol and LDL-C. The likelihood of developing diabetes was 40% higher when LDL-C was greater than 2.3 mmol/L than in patients with LDL-C less than 2.3 mmol/L.

Association Between Central Obesity And Chronic Kidney Disease

Background: Chronic kidney disease (CKD) is increasingly recognized as a global public health concern which is one of the leading cause of death and disability. Central obesity increases the risk of developing major risk factors for chronic kidney disease like diabetes and hypertension. Methods: This case-control study was conducted between January and December, 2019. Fifty-five cases and fiftyfive age and sex matched controls were interviewed by a pretested semi-structured questionnaire. Medical records of the respondents were reviewed and a checklist was used to document after measuring height, weight, waist circumference and blood pressure. Results: The study showed that hypertension and diabetes mellitus were present in significantly higher proportions among the CKD respondents compared to the non-CKD respondents (P &lt; 0.001). Cases were 11.5 times (OR 11.425; 95% CI 4.575-28.528) more likely to be hypertensive than controls. Cases were also 8.5 times (OR 8.469; 95% CI 3.119-23.000) more likely to be diabetic than controls. Among the all respondents, the proportion of central obesity was 40.9% while comparatively higher proportions were found in cases (45.5%) than in controls (36.4%). No significant association was found statistically between central obesity and chronic kidney disease. But diabetes mellitus and BMI were significantly associated with central obesity (P &lt; 0.05). Centrally obese respondents were 2.5 times (OR 2.436; 95% CI 1.066-5.566) more likely to be diabetic than non-obese respondents. Lifestyle related risk factors such as consumption of tobacco both cigarette smoking and smokeless tobacco, alcohol consumption and body mass index (BMI) were also not associated with chronic kidney disease. Conclusion: As central obesity was associated with major risk factors of chronic kidney disease, so weight loss should be encouraged in obese subjects to decrease the risk of CKD. JOPSOM 2023; 42(1):7-13