Recent advancements in multi-omics technologies have provided unprecedented opportunities to identify biomarkers associated with prediabetes, offering novel insights into its diagnosis and management. This review synthesizes the latest findings on prediabetes from multiple omics domains, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and radiomics. We explore how these technologies elucidate the molecular and cellular mechanisms underlying prediabetes and analyze potential biomarkers with predictive value in disease progression. Integrating multi-omics data helps address the limitations of traditional diagnostic methods, enabling early detection, personalized interventions, and improved patient outcomes. However, challenges such as data integration, standardization, and clinical validation and translation remain to be resolved. Future research leveraging artificial intelligence and machine learning is expected to further enhance the predictive power of multi-omics technologies, contributing to the precision diagnosis and tailored management of prediabetes.

Hepatic mitochondrial respiration is higher in steatosis, but lower in overt type 2 diabetes. We hypothesized that hepatic oxidative phosphorylation (OXPHOS) capacity increases with a greater degree of insulin resistance in obesity, independent of other metabolic diseases.

We analyzed 65 humans without diabetes (BMI 50 ± 7 kg/m2, hemoglobin A1c 5.5 ± 0.4%) undergoing bariatric surgery. Metabolic dysfunction-associated steatotic liver disease (MASLD) stages were assessed by histology, whole-body insulin sensitivity (PREDIcted-M index) by oral glucose tolerance tests, and maximal ADP-stimulated mitochondrial OXPHOS capacity by high-resolution respirometry of liver samples.

Prediabetes was present in 30 participants and MASLD in 46 participants, of whom 25 had metabolic dysfunction-associated steatohepatitis, and seven had F2-F3 fibrosis. While simple regression did not detect an association of insulin sensitivity with hepatic OXPHOS capacity, interaction analyses revealed that the regression coefficient of OXPHOS capacity depended on fasting plasma glucose (FPG) and liver lipid content. Interestingly, the respective slopes were negative for FPG ≤100 mg/dl, but positive for FPG >100 mg/dl. Liver lipid content displayed similar behavior, with a threshold value of 24%. Post-challenge glycemia affected the association between insulin sensitivity and OXPHOS capacity normalized for citrate synthase activity. Presence of prediabetes affected hepatic insulin signaling, mitochondrial dynamics and fibrosis prevalence, while the presence of MASLD was associated with increases in biomarkers of hepatic inflammation, cell damage and lipid peroxidation in people with normal glucose tolerance.

Increasing liver lipid contents and plasma glucose concentrations, even in the non-diabetic range, are associated with a progressive decline of hepatic mitochondrial adaptation in people with obesity and insulin resistance.

Mechanisms underlying the progression of metabolic dysfunction-associated steatotic liver disease (MASLD) are still unclear, but a better understanding of the pathogenesis of MASLD is essential for the development of targeted treatments. Adaptation of liver oxidative capacity was found to be impaired in people with diabetes and MASLD or liver fibrosis. Glycemia and liver lipid content affect the adaptation of hepatic oxidative capacity to insulin resistance in obesity. These results highlight the relevance of metabolically active drugs in individuals with grade 3 obesity and early MASLD. CLINTRIALS.

NCT01477957.

Diabetes is a well-known risk factor for dementia. We investigated the association between (pre)diabetes and older brain age and whether this can be attenuated by modifiable lifestyle behaviors.

The study included 31,229 dementia-free adults from the UK Biobank between the ages of 40 and 70 years. Glycemic status (normoglycemia, prediabetes, or diabetes) was ascertained based on medical history, medication use, and HbA1c measured at baseline. Information on cardiometabolic risk factors (obesity, hypertension, low HDL, and high triglycerides) and lifestyle behaviors (smoking, drinking, and physical activity) was also collected at baseline. Participants underwent up to two brain MRI scans over 11 years of follow-up. Brain age was estimated using a machine learning model based on 1,079 brain MRI phenotypes and used to calculate brain age gap (BAG; i.e., brain age minus chronological age).

At baseline, 13,518 participants (43.3%) had prediabetes and 1,149 (3.7%) had diabetes. Prediabetes (β = 0.22 [95% CI 0.10, 0.34]) and diabetes (2.01 [1.70, 2.32]) were both associated with significantly higher BAG, and diabetes was further associated with significant increase in BAG over time (0.27 [0.01, 0.53]). The association between (pre)diabetes and higher BAG was more pronounced in men and in people with two or more cardiometabolic risk factors. In joint exposure analysis, having a healthy lifestyle (i.e., no smoking, no heavy drinking, and high physical activity) significantly attenuated the diabetes-BAG association.

Diabetes and even prediabetes are associated with accelerated brain aging, especially among men and people with poor cardiometabolic health. However, a healthy lifestyle may counteract this.

Natural flavonoids exert many potential health benefits, including anti-hyperglycaemic effects. However, the effects of gossypetin (GTIN) on glucose homeostasis in pre-diabetes have not yet been investigated. This study examined the effects of GTIN on key markers of glucose homeostasis in a diet-induced pre-diabetic rat model. Pre-diabetes was induced by allowing the animals to feed on a high-fat high-carbohydrate (HFHC) diet supplemented with 15% fructose water for 20 weeks. Following pre-diabetes induction, the pre-diabetic animals were sub-divided into five groups (n = 6), where they were either orally treated with GTIN (15 mg/kg) or metformin (MET) (500 mg/kg), both with and without dietary intervention, over a 12-week period. The results demonstrated that animals in the untreated pre-diabetic (PD) control group exhibited significantly higher fasting and postprandial blood glucose levels, as well as elevated plasma insulin concentrations and increased homeostatic model assessment for insulin resistance (HOMA2-IR) index, relative to the non-pre-diabetic (NPD) group. Similarly, increased caloric intake, body weight and plasma ghrelin levels were observed in the PD control group. Notably, these parameters were significantly reduced in the PD animals receiving GTIN treatment. Additionally, glycogen levels in the liver and skeletal muscle, which were disturbed in the PD control group, showed significant improvement in both GTIN-treated groups. These findings may suggest that GTIN administration, with or without dietary modifications, may offer therapeutic benefits in ameliorating glucose homeostasis disturbances associated with the PD state.

The metabolite α-hydroxybutyrate (α-HB) is an important marker of insulin resistance and impaired glucose tolerance allowing to identify patients at risk of developing diabetes and related metabolic disorders before any symptoms become apparent. At present, its exact quantification requires mass spectrometry (LC-MS), which is not compatible with routine laboratory use. Accordingly, a simple enzymatic-based method was assessed and its applicability and measuring accuracy compared with LC-MS was investigated.

Standards, serum, and plasma samples containing α-HB were prepared with routine procedures and their α-HB contents measured with the XpressGT® enzymatic test kit photometrically or with LC-MS and multiple reaction monitoring.

α-HB detection with XpressGT® yielded highly linear calibration curves and 102 % recovery of stocks added to commercial samples. Stability of the analyte in serum and plasma samples prepared with various anti-coagulants was >90 % after 46 h for several widely used preparations and recovery after 3 freeze-thaw cycles was ≥95 % with these anti-coagulants. A direct comparison of 75 samples indicated very good agreement of α-HB levels determined by both methods, 86 % of XpressGT® samples being within ±20 % of LC-MS values and even 93 % within ±20 % considering only samples above 30 μM concentration.

XpressGT®-based detection of α-HB is an easily applicable method which can be used for accurate and reliable quantification of the metabolite in clinical practice. Routine α-HB determination in patients at risk of developing diabetes would allow early establishment of preventive measures or pharmacological intervention reducing the risk for the onset of serious diabetes-related health problems.

To estimate the incidence of T2DM and assess the effect of pre-T2DM (isolated impaired fasting glucose [iIFG], isolated impaired glucose tolerance [iIGT] or both) on progress to T2DM in the adult population of Madrid.

Population-based cohort comprising 1,219 participants (560 normoglycaemic and 659 preT2DM [418 iIFG, 70 iIGT or 171 IFG-IGT]). T2DM was defined based on fasting plasma glucose or HbA1c or use of glucose-lowering medication. We used a Cox model with normoglycaemia as reference category.

During 7.26 years of follow-up, the unadjusted incidence of T2DM was 11.21 per 1000 person-years (95 %CI, 9.09-13.68) for the whole population, 5.60 (3.55-8.41) for normoglycaemic participants and 16.28 (12.78-20.43) for pre-T2DM participants. After controlling for potential confounding factors, the baseline glycaemic status was associated with higher primary effect on developing T2DM was iIGT (HR = 3.96 [95 %CI, 1.93-8.10]) and IFG-IGT (3.42 [1.92-6.08]). The HR for iIFG was 1.67 (0.96-2.90). Obesity, as secondary effect, was strongly significantly associated (HR = 2.50 [1.30-4.86]).

Our incidence of T2DM is consistent with that reported elsewhere in Spain. While baseline iIGT and IFG-IGT behaved a primary effect for progression to T2DM, iIFG showed a trend in this direction.

As the burden of type 2 diabetes (T2D) continues to escalate globally, there is a growing need for novel, less-invasive biomarkers capable of early diabetes detection and monitoring of disease progression. Liquid biopsy, recognized for its minimally invasive nature, is increasingly being applied beyond oncology, and nevertheless shows its potential when the collection of the tissue biopsy is not possible. This diagnostic approach involves utilizing liquid biopsy markers such as cell-free nucleic acids, extracellular vesicles, and diverse metabolites for the molecular diagnosis of T2D and its related complications. In this context, we thoroughly examine recent developments in T2D liquid biopsy research. Additionally, we discuss the primary challenges and future prospects of employing liquid biopsy in the management of T2D. Prognosis, diagnosis and monitoring of T2D through liquid biopsy could be a game-changing technique for personalized diabetes management.

The progression from prediabetes to type-2 diabetes depends on multiple pathophysiological, clinical, and epidemiological factors that generally overlap. Both insulin resistance and decreased insulin secretion are considered to be the main causes. The diagnosis and approach to the prediabetic patient are heterogeneous. There is no agreement on the diagnostic criteria to identify prediabetic subjects or the approach to those with insufficient responses to treatment, with respect to regression to normal glycemic values or the prevention of complications. The stratification of prediabetic patients, considering the indicators of impaired fasting glucose, impaired glucose tolerance, or HbA1c, can help to identify the sub-phenotypes of subjects at risk for T2DM. However, considering other associated risk factors, such as impaired lipid profiles, or risk scores, such as the Finnish Diabetes Risk Score, may improve classification. Nevertheless, we still do not have enough information regarding cardiovascular risk reduction. The sub-phenotyping of subjects with prediabetes may provide an opportunity to improve the screening and management of cardiometabolic risk in subjects with prediabetes.

Prediabetes is associated with an increased risk of cardiovascular diseases and all-cause mortality. Rare research in China has evaluated the prevalence of prediabetes among children and adolescents using the HbA1c criterion or the combined FPG-or-HbA1c diagnostic criterion, and researchers paid no attention to the distributions of blood glucose in Shenzhen, especially for juveniles.

We conducted a school-based cross-sectional study based on the first-year students from 17 primary, middle, and high schools. Prediabetes was defined as FPG of 5.6-6.9 mmol/L or HbA1c of 5.7%-6.4%. The crude and standardized prevalence of prediabetes with 95% confidence interval (95% CI) was estimated.

A total of 7519 participants, aged 6 to 17 years, were included. For all subjects, the crude prevalence (95% CI) of prediabetes was 1.49% (1.21-1.77), 8.72% (8.08-9.36), and 9.80% (9.13-10.47) by the FPG-only, HbA1c-only, and FPG-or-HbA1c criteria, respectively. Based on the 2010 Shenzhen census population, the standardized prevalence was 1.56% (males 1.85%, females 1.19%), 11.05% (males 11.47%, females 10.53%), and 12.19% (males 13.01%, females 11.15%) by the corresponding criteria. The proportion of prediabetes was higher for males than females, and the prevalence decreased with grade for males but increased for females. The association of BMI and prediabetes was U-shaped curve, indicating higher rates of prediabetes for underweight and obesity people.

The blood glucose status of children and adolescents in Shenzhen is worrisome, and the early detection and management of prediabetes are imperative.

The association between diabetes and frozen shoulder is well established. However, the data regarding prediabetes and primary frozen shoulder (PFS) are still lacking.

In a prospective study, 158 patients with PFS were included. The prediabetes status was ascertained by estimating serum hemoglobin A1c (HbA1c) levels in patients with PFS. According to the level of HbA1c, patients were classified into normoglycemic, prediabetic, and diabetic. In addition, random blood sugar (RBS) was also performed.

Out of 158 participants, 84 (53.2%) were male and 74 (46.8%) were female. Nine patients had bilateral shoulder involvement, and all were diabetics; 47.5% (n = 75) of the patients were in the age group of 51-60 years, 16.5% (n = 26) of the participants were normoglycemic, 37.3% (n = 59) were prediabetics, and 46.2% (n = 73) were diabetics. The difference in mean HbA1c values between the 3 groups was statistically significant (P < .001). However, there was no statistical difference in various age groups (P = .86) or gender (P = .68) between normoglycemics, prediabetics, and diabetics. The difference in mean RBS values between diabetic-nondiabetic and diabetic-prediabetic groups were statistically significant (P < .001), whereas no significant difference was detected between nondiabetic and prediabetic (P = .355).

The prevalence of prediabetes is 37.5% in patients with PFS. Single-point HbA1c estimation is an acceptable tool to detect prediabetes, whereas RBS estimation should not be used to detect prediabetes.

Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting.

In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status.

The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001).

Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Prediabetes and diabetes mellitus (DM) are complications in adult patients with transfusion-dependent β-thalassemia (β-TDT), with their incidence increasing with age.

This retrospective observational study describes the glycemic trajectories and evaluates predictive indices of β-cell function and insulin sensitivity/resistance in β-TDT patients with prediabetes, both in a steady state and during 3-h oral glucose tolerance test (OGTT), in order to identify patients at high risk for incipient diabetes.

The study was mainly conducted at the Pediatric and Adolescent Outpatient Clinic, Quisisana Hospital, Ferrara (Italy), in collaboration with thalassemia referring centers across Italy.

The study included 11 β-TDT (aged 15.11-31.10 years) with prediabetes. Methods: The ADA criteria for the diagnosis of glucose dysregulation were adopted. Investigations included evaluating plasma glucose levels and insulin secretion, analyzing glycemic trajectories and indices of β-cell function, and insulin sensitivity/resistance assessed in steady state and during OGTT.

The duration of progression from prediabetes to DM, expressed in years, showed a positive direct correlation with corrected insulin response (CIR-30 = r: 0.7606, P: 0.0065), insulinogenic index (IGI 0-120 = r: 0.6121, P:0.045), oral disposition index (oDI = r: 0.7119, P:0.013), insulin growth factor-1 (IGF-1= r: 0.6246, P: 0.039) and an inverse linear correlation with serum ferritin (SF = r: -0.7197, P: 0.012). The number of patients with 1-hour post-load PG value ≥ 155 mg/dL ( ≥ 8.6 mmol/L) was at -4 years: 4/9 (44.4%); -3 years: 8/9 (88.8%); - 2 years: 7/10 (70 %) and at -1 year: 11/11 (100%) (PG range:162-217 mg/dL).

A progressive increase in 1-hour PG in response to OGTT is associated with progressive β-cell failure, peripheral resistance to insulin action, and reduced oDI and may be considered a relevant marker for incipient DM in β-TDT patients with prediabetes.

Background Age- and gender-based differences in diabetes demographic characteristics have been studied in many types of research. These differences extend further to diabetes-related comorbidities. Dyslipidemia is a common complication associated with diabetes and causes a substantial increase in cardiovascular morbidity. The study aims to compare the pattern of dyslipidemia between males and females among different age categories in newly diagnosed type 2 diabetes mellitus (T2DM). Methodology A retrospective database study was conducted at Faiha Specialized Diabetes, Endocrine, and Metabolism Center (FDEMC), Basrah, Southern Iraq. We included adult patients with newly diagnosed and drug naïve T2DM between January 2018 and October 2022. Patients' data in the form of body mass index (BMI), hemoglobin A1c (HbA1c), fasting blood glucose (FBG), random blood glucose (RBG), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were used for comparisons. Results Below the age of 35, males exhibited significantly higher levels of HbA1c, FBG, and TG compared to females, along with a significantly lower level of HDL-C. However, there were no significant differences in BMI, RBG, TC, and LDL-C. Between the ages of 35 and 44, females in this study demonstrated significantly higher BMI and HDL-C levels, while males exhibited higher levels of HbA1c, FBG, RBG, and TG. However, there were no significant differences observed in TC and LDL-C levels. Similar results were found among the age group 45 to 55, with the only exception being FBG, which became nonsignificant. In patients between 55 and 64 years old, BMI, HDL-C, and TC were significantly higher in females (P < 0.05). In patients aged above 65 years, BMI and HDL-C remained significantly higher in females, while RBG was significantly higher in males. No significant differences were observed among other parameters (HbA1c, TG, TC, and LDL-C). Conclusions In patients aged 54 years and younger, males were significantly more likely to have severe hyperglycemia, higher TG, and lower HDL-C compared to females at the time of T2DM diagnosis. In older patients, this pattern is lost, with only a significantly lower HDL-C observed.

Patients with chronic graft-versus-host disease (cGVHD) are at heightened risk for components of metabolic syndrome (MetS), yet the prevalence and impact of MetS in the cGVHD patient population remain unknown. Adult patients (n = 229) with cGVHD enrolled in the cross-sectional NIH cGVHD Natural History Study (NCT00092235) were evaluated for MetS at enrollment and for variables associated with MetS. A majority (54.1%, 124/229) of the cohort met the diagnostic criteria for MetS. Patients with higher body mass index and lower performance status scores were more likely to have MetS (P < 0.0001; P = 0.026; respectively). Higher circulating erythrocyte sedimentation rate, C-reactive protein, and creatinine concentrations, along with lower estimated glomerular filtration rate, were associated with MetS (P < 0.001; P < 0.004; P = 0.02; P = 0.002; respectively). Patients with MetS compared to patients without MetS had no statistical differences in survival or NRM (5-year OS: 64% [95% CI: 54.8-71.8%] vs. 75.1% [95% CI: 65.6-82.3%]; respectively; overall P = 0.20; 5-year NRM: 21.7% [95% CI: 13.6-30.9%] vs. 10.1% [95% CI: 4.4-18.7%]; respectively; overall P = 0.12). Additionally, there was no difference in cGVHD severity between the two groups. Given the high prevalence of MetS in this cohort, clinicians should screen for its presence before it develops into comorbidities that complicate the course of cGVHD treatment.

A state of impaired glucose tolerance is called prediabetes. The diagnosis of prediabetes is controversial, yet it still puts a person at risk for developing diabetes. The ankle-brachial index (ABI) is useful for identifying persons at risk for peripheral artery disease and for diagnosing the condition in those who have symptoms in their lower extremities and subclinical atherosclerosis. This study highlights ABI and its correlation with cardiovascular risk factors like lipid profile and anthropometric measurement including neck circumference in prediabetes so that primary care physicians may be able to diagnose early before advancing to diabetes.

This cross-sectional study of 2 years duration from December 2020 to September 2022 was conducted in the Department of Medicine, at a tertiary care teaching hospital situated in a rural area. Patients with pre-diabetes were enrolled and Ankle Brachial Index was calculated. The correlation of ABI with anthropometric measures and lipid profile was assessed.

On calculating ABI by manual method 21% which is 42 out of 200 had low ABI (<0.9). On the other hand, on calculating ABI by probe method low range of ABI was found to be 37% which is 74 patients out of 200. There was a significant correlation between ABI and body mass index and lipid profile. The diagnostic performance of ABI < 0.9 had 56.8% sensitivity and 100.0% specificity.

ABI can be used as a noninvasive and cost-effective modality for assessing subclinical atherosclerosis in patients with prediabetes and thus prevent its morbid complications even assessed at the primary care physician level.

We evaluate the automatic identification of type 2 diabetes from neck-to-knee, two-point Dixon MRI scans with 3D convolutional neural networks on a large, population-based dataset. To this end, we assess the best combination of MRI contrasts and stations for diabetes prediction, and the benefit of integrating risk factors.

Subjects with type 2 diabetes mellitus have been identified in the prospective UK Biobank Imaging study, and a matched control sample has been created to avoid confounding bias. Five-fold cross-validation is used for the evaluation. All scans from the two-point Dixon neck-to-knee sequence have been standardized. A neural network that considers multi-channel MRI input was developed and integrates clinical information in tabular format. An ensemble strategy is used to combine multi-station MRI predictions. A subset with quantitative fat measurements is identified for comparison to prior approaches.

MRI scans from 3406 subjects (mean age, 66.2 years ± 7.1 [standard deviation]; 1128 women) were analyzed with 1703 diabetics. A balanced accuracy of 78.7 %, AUC ROC of 0.872, and an average precision of 0.878 was obtained for the classification of diabetes. The ensemble over multiple Dixon MRI stations yields better performance than selecting the individually best station. Moreover, combining fat and water scans as multi-channel inputs to the networks improves upon just using single contrasts as input. Integrating clinical information about known risk factors of diabetes in the network boosts the performance across all stations and the ensemble. The neural network achieved superior results compared to the prediction based on quantitative MRI measurements.

The developed deep learning model accurately predicted type 2 diabetes from neck-to-knee two-point Dixon MRI scans.

The objective is to investigate the prevalence of pre-diabetes in Namibia and South Africa and to determine sociodemographic correlates of disease using population data.

Cross-sectional study.

Demographic and Health Survey for emerging (Namibia) and established (South Africa) economies in Sub-Saharan Africa collected laboratory data that allowed determination of pre-diabetes status.

3141 adults over age 18 from the 2013 Namibia survey, weighted to a population of 2176, and 4964 adults over age 18 from the 2016 South Africa survey, weighted to a population of 4627 had blood glucose/glycated haemoglobin (HbA1c) and diabetes information were included in the analysis.

Pre-diabetes was defined as not being diagnosed with diabetes and having a blood sugar measurement of 100-125 mg/dL in Namibia or an HbA1c measurement of 5.7%-6.4%. Logistic models were run for each country separately, with pre-diabetes as the outcome and a series of sociodemographic variables (age, gender, urban/rural residence, number of children, employment status, wealth index, education level, and ethnicity (in South Africa) or religion (in Namibia)) entered as variables to investigate the independent relationship of each.

The weighted prevalence of pre-diabetes was 18.7% in Namibia and 70.1% in South Africa. Rural residence was independently associated with higher odds of pre-diabetes in Namibia (1.47, 95% CI 1.05 to 2.06), while both younger age (0.98, 95% CI 0.97 to 0.99) and urban residence (0.80, 95% CI 0.66 to 0.99) were independently associated with odds of pre-diabetes in South Africa.

The prevalence of pre-diabetes was 18.7% in Namibia and 70.1% in South Africa. Correlates of pre-diabetes differed between the two countries with rural residents having higher odds of pre-diabetes in Namibia and urban residents with higher odds in South Africa. Aggressive interventions, including population level education and awareness programmes, and individual level education and lifestyle interventions that account for country-specific contextual factors are urgently needed to prevent progression to diabetes.

This article illustrates how dietary flaxseed can be used to reduce cancer risk, specifically by attenuating obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD). We utilize a targeted metabolomics dataset in combination with a reanalysis of past work to investigate the "metabo-bioenergetic" adaptations that occur in White Leghorn laying hens while consuming dietary flaxseed. Recently, we revealed how the anti-vitamin B6 effects of flaxseed augment one-carbon metabolism in a manner that accelerates S-adenosylmethionine (SAM) biosynthesis. Researchers recently showed that accelerated SAM biosynthesis activates the cell's master energy sensor, AMP-activated protein kinase (AMPK). Our paper provides evidence that flaxseed upregulates mitochondrial fatty acid oxidation and glycolysis in liver, concomitant with the attenuation of lipogenesis and polyamine biosynthesis. Defatted flaxseed likely functions as a metformin homologue by upregulating hepatic glucose uptake and pyruvate flux through the pyruvate dehydrogenase complex (PDC) in laying hens. In contrast, whole flaxseed appears to attenuate liver steatosis and body mass by modifying mitochondrial fatty acid oxidation and lipogenesis. Several acylcarnitine moieties indicate Randle cycle adaptations that protect mitochondria from metabolic overload when hens consume flaxseed. We also discuss a paradoxical finding whereby flaxseed induces the highest glycated hemoglobin percentage (HbA1c%) ever recorded in birds, and we suspect that hyperglycemia is not the cause. In conclusion, flaxseed modifies bioenergetic pathways to attenuate the risk of obesity, type 2 diabetes, and NAFLD, possibly downstream of SAM biosynthesis. These findings, if reproducible in humans, can be used to lower cancer risk within the general population.

Type 2 diabetes mellitus (T2DM) is a disease of public health importance globally with an increasing burden of undiagnosed pre-diabetes and diabetes in low- and middle-income countries, Nigeria in particular. Pre-diabetes and diabetes are established risk factors for cardiovascular complications. However, data are scanty on the current prevalence of these conditions in Nigeria, based on haemoglobin A1c (HbA1c) diagnosis as recommended by the WHO in 2009. We aimed to determine the prevalence of pre-diabetes, diabetes, and undiagnosed diabetes among the adult population of Nigeria using HbA1c.

A cross-sectional, multi-site population study was carried out in selected states in Nigeria (namely, Ekiti, Lagos, Osun, Oyo, and Kwara states) involving 2,708 adults (≥18 years) in rural and urban community dwellers, without prior diagnosis of pre-diabetes or diabetes. Participants with ongoing acute or debilitating illnesses were excluded. Data were collected using an interviewer-administered pretested, semi-structured questionnaire. Socio-demographic, clinical (weight, height, blood pressure, etc.), and laboratory characteristics of participants including HbA1c were obtained. Data were analysed using STATA version 16.

The mean age of participants was 48.1 ± 15.8 years, and 65.5% were female. The overall prevalence of pre-diabetes and undiagnosed diabetes was 40.5% and 10.7%, respectively, while the prevalence of high blood pressure was 36.7%. The prevalence of pre-diabetes was the highest in Lagos (48.1%) and the lowest in Ekiti (36.7%), while the prevalence of diabetes was the highest in Kwara (14.2%) and the lowest in Ekiti (10%). There was a significant association between age of the participants (p< 0.001), gender (p = 0.009), educational status (p = 0.008), occupation (p< 0.001), tribe (p = 0.004), marital status (p< 0.001), blood pressure (p< 0.001), and their diabetic or pre-diabetic status. Independent predictors of diabetes and pre-diabetes include excess weight gain, sedentary living, and ageing. Participants within the age group 45-54 years had the highest total prevalence (26.6%) of pre-diabetes and diabetes.

Over half of the respondents had pre-diabetes and diabetes, with a high prevalence of undiagnosed diabetes. A nationwide screening campaign will promote early detection of pre-diabetes and undiagnosed diabetes among adult Nigerians. Health education campaigns could be an effective tool in community settings to improve knowledge of the risk factors for diabetes to reduce the prevalence of dysglycaemia.

Papaya (Carica papaya L.) is a highly nutritious and less-caloric fruit, commonly consumed for its minerals and vitamins and hence may help in controlling obesity and abdominal discomforts. The present study investigated the hypolipidaemic effects of papaya juice extract on male Albino Wistar rats (7 weeks old; 185 ± 17 g) fed on a high fat and fructose diet (HFFD) for 6 weeks. The rats were divided into groups I-IV of five rats each and fed on either a HFFD (i.e. the Control), HFFD + 200 mg papaya, HFFD + 350 mg papaya or a HFFD + 500 mg papaya. On day 34, after an overnight fast, blood samples were obtained by cardiac puncture under 99⋅8 % Chloroform anaesthesia for the determination of serum triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c) and high-density cholesterol (HDL-c). The atherogenic (AI) and coronary risk (CRI) indices were also calculated. Statistical analysis was performed using ANOVA where means were separated using Tukey's HSD test. Resulted showed that all rats given papaya juice had an increasing, non-significant HDL-c and reduced LDL-c levels while rats fed on HFFD had the highest TC (53⋅2 mg/dl) and TG (37⋅6 mg/dl) levels. Papaya juice statistically reduced the AI and CRI of the rats. In conclusion, consumption of HFFD + 500 mg was the most effective in the reduction of rats' blood lipids and fats, due to its anti-obesity and hypolipidaemic properties, thus can be used in the management of dyspilidaemic disorders.

It is difficult to predict the risk of developing atherosclerotic cardiovascular disease in subjects with prediabetes and obesity. The aim of this study was to assess risk factors for coronary artery calcifications (CACs) and the development of type 2 diabetes (T2D) and coronary vascular events (CVEs) after 7 years in 100 overweight or obese persons with prediabetes, according to the baseline coronary artery calcium score (CACS).

Lipids, HbA1c, uric acid, and creatinine were assessed. Glucose, insulin, and c-peptide were determined during an oral glucose tolerance test. Multi-sliced computerized tomography with evaluation of CACS was performed. After 7 years, the subjects were assessed for T2D/CVE.

CACs were present in 59 subjects. No single biochemical marker could predict presence of a CAC. After 7 years, T2D developed in 55 subjects (61.8% initially had both IFG and IGT). A gain in weight was the only contributing factor for T2D. Nineteen subjects developed a CVE; increased initial clustering of HOMA-IR > 1.9, LDL > 2.6, and mmol/Land TGL > 1.7 mmol/L and higher CACS were present in that group.

No risk factors for CACs could be identified. A gain in weight is associated with T2D development, as are higher CACS and clustering of high LDL+TGL+HOMA-IR with CVEs.

Adoption of the International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria for diagnosis of gestational diabetes mellitus (GDM) varies worldwide. Early detection of women at increased risk of developing type 2 diabetes mellitus (T2DM) following GDM enables initiation of measures to delay disease onset.

To determine the 4-year cumulative incidence and risk factors for developing abnormal glucose tolerance (AGT) among women with previous GDM using modified IADPSG criteria. Additionally, to review post-natal attendance at diabetes screening and the impact of post-partum lifestyle modifications and breastfeeding on the risk of T2DM development.

Four hundred twenty-six women with a prior history of GDM were invited to participate in the study, 4 years after the index pregnancy. The following were completed: body measurements, oral glucose tolerance test (OGTT), glycated haemoglobin (HbA1c), vitamin D, and other biochemistry measurements. Participants also completed a lifestyle questionnaire.

Of the 74 women who participated, 15 (20%) had AGT. Predictive factors for AGT development were as follows: fasting glucose levels (p = 0.004), HbA1c (p = 0.008) at GDM diagnosis, and early pregnancy BMI (p = 0.001). Thirty-three (45%) women had not attended their postnatal screening. The odds ratio of the association between breastfeeding and AGT development was 0.16 (95% CI: 0.05 to 0.53).

The proportion of women who develop AGT after a diagnosis of GDM remains high. The factors associated with progression to AGT are available at GDM diagnosis. Preventing AGT in this group is possible by supporting breastfeeding. Attendance at post-natal screening should also be encouraged.

To examine the differences between HbA_1c and glucose related variables in predicting weight loss and glycaemic changes following 8 weeks of low energy diet (LED) in individuals with overweight and hyperglycaemia.

2178 individuals with ADA-defined pre-diabetes - impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT) - who started an 8 week LED weight loss diet, were included in this analysis. Participants were enrolled in the PREVIEW (PREVention of diabetes through lifestyle interventions and population studies In Europe and around the World) clinical trial. Multivariable linear mixed effects regression models and generalised additive mixed effect logistic models were used.

Only 1 in 3 participants (33%) had HbA_1c levels defined as pre-diabetes. Neither baseline HbA_1c, IFG or IGT were associated with body weight change at 8 weeks. Higher baseline body weight, baseline fasting insulin and weight loss predicted normalisation of fasting plasma glucose (FPG), whilst higher baseline fasting insulin, C-reactive protein (hsCRP) and older age predicted normalisation of HbA_1c. Additionally, male sex and higher baseline BMI, body fat and energy intake were positively associated with weight loss, whereas greater age and higher HDL-cholesterol predicted less weight loss.

Whilst neither HbA_1c nor fasting glucose predicts short-term weight loss success, both may impact the metabolic response to rapid weight loss. We propose a role of inflammation versus total body adiposity since these variables are independent predictors of the normalisation of HbA_1c and fasting glucose, respectively.

Prevalence of prediabetes and type 2 diabetes mellitus (T2DM) is increasing worldwide. The objective of this study was to determine the proportion of people in Northern Iceland with prediabetes, at risk of developing T2DM or with manifest undiagnosed T2DM, as this information is lacking in Iceland.

A cross-sectional study. Clients of the three largest primary health care centres in the Health Care Institution of North Iceland (HSN) were invited to participate if fulfilling the following inclusion criteria: a) aged between 18 and 75 years, b) not diagnosed with diabetes, c) speaking and understanding Icelandic or English fluently and d) living in the included service area. Data collection took place via face-to-face interviews between 1 March 2020 and 15 May 2021. Participation included answering the Finnish Diabetes Risk Score (FINDRISC), measuring the HbA1c levels and background information.

Of the 220 participants, 65.9% were women. The mean age was 52.1 years (SD ± 14.1) and FINDRISC scores were as follows: 47.3% scored ≤8 points, 37.2% scored between 9 and 14 points, and 15.5% scored between 15 and 26 points. The mean HbA1c levels in mmol/mol, were 35.5 (SD ± 3.9) for men and 34.4 (SD ± 3.4) for women, ranging from 24 to 47. Body mass index ≥30 kg/m² was found in 32% of men and 35.9% of women. Prevalence of prediabetes in this cohort was 13.2%. None of the participants had undiagnosed T2DM. Best sensitivity and specificity for finding prediabetes was by using cut-off points of ≥11 on FINDRISC, which gave a ROC curve of 0.814.

The FINDRISC is a non-invasive and easily applied screening instrument for prediabetes. Used in advance of other more expensive and invasive testing, it can enable earlier intervention by assisting decision making, health promotion actions and prevention of the disease burden within primary health care.

This study is a pre-phase of the registered study "Effectiveness of Nurse-coordinated Follow up Program in Primary Care for People at risk of T2DM" at www.

gov (NCT01688359). Registered 30 December 2020.

Postprandial glycemia (PPG) predicts cardiovascular disease, and short-term physical inactivity increases PPG in young, active adults. Whether this occurs in older, active adults who may be more prone to bouts of inactivity is unknown. This study determined if postprandial interstitial glucose (PPIG) was impaired in active older adults following the removal of exercise for 3 days (NOEX) compared to active young adults. In this randomized, crossover study, 11 older (69.1 ± 1.9 years) and 9 young (32.8 ± 1.8 years) habitually active (≥90 min/week of exercise) adults completed 3-days of NOEX and 3-days of normal habitual exercise (EX), separated by ≥1 week. Diet was standardized across phases. Glycemic control (3-day average) was assessed via continuous glucose monitoring during both phases. Significant main effects of age and phase were detected (p < 0.05), but no interaction was found for steps/day (p > 0.05) (old EX: 6283 ± 607, old NOEX: 2380 ± 382 and young EX: 8798 ± 623, young NOEX: 4075 ± 516 steps/day). Significant main effects of age (p = 0.002) and time (p < 0.001) existed for 1-h PPIG, but no effect of phase or interactions was found (p > 0.05). Significant main effects (p < 0.05) of age (old: 114 ± 1 mg/dl, young: 106 ± 1 mg/dl), phase (NOEX: 112 ± 1 mg/dl, EX: 108 ± 1 mg/dl), and time (0 min: 100 ± 2, 30 min: 118 ± 2, 60 min: 116 ± 2, 90 min: 111 ± 2, 120 min: 108 ± 2 mg/dl) in 2-h PPIG were detected, but no interaction was found (p > 0.05). However, only significant main effects of phase (NOEX: 14 ± 1 and EX:12 ± 1, p > 0.05) were found for 24-h blood glucose standard deviation. Older adults appear to have impaired glycemic control compared to young adults and exercise removal impairs glycemic control in both populations. Yet, the impairment in glycemic control with exercise removal is not different between old and young adults.

Chronically elevated alpha-2-macroglobulin (A2MG) in the blood has been correlated with diabetes and the HbA1c profile; however, no systematic review has been conducted to evaluate the association of A2MG salivary levels and glycemia or HbA1c levels in diabetes mellitus type 2 (DM2) patients.

To evaluate whether A2MG salivary levels are related to the glycemia or HbA1c levels in DM2 patients.

Systematic review developed at Universidade Federal de Uberlândia (UFU), Brazil.

Eight databases were used as research sources. The eligibility criteria included studies that reported data regarding mean salivary A2MG and the correlation between glycemia and/or HbA1c levels of DM2 subjects (uncontrolled and well-controlled) and non-diabetic subjects. The risk of bias of the studies selected was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools for use in JBI systematic reviews. Pooled correlation coefficients were estimated using the Hunter-Schmidt method. Study estimates were weighted according to their sample size, and heterogeneity was calculated using the chi-square statistic.

Four studies on DM2 patients were included in this systematic review after careful analysis of 1482 studies. Three studies compared A2MG with HbA1c and glycemia. Overall, the correlation between A2MG and HbA1c was strong (r = 0.838). In contrast, the correlation between A2MG and glycemia was low (r = 0.354).

The strong association between HbA1C and salivary A2MG suggests that this salivary protein has the potential to be a surrogate for HbA1C, if corroboratory further evidence is obtained through large-scale studies.

Herein, a case study of an individual with fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), and postprandial blood glucose (PBG) measures from the 3 years preceding their type 1 diabetes mellitus diagnosis is used to highlight discordance among these common diagnostic tests. Data from the patient's own records, participation in clinical research, and healthcare provider were collated. Measures of FBG (90-160 mg/dL) and PBG (195-247 mg/dL) were elevated for 3 years with a normal HbA1c (5.0-5.4%) and without any symptoms. Overt symptoms, including polyuria, polydipsia, and unexplained weight loss, manifested 3 years later prompting the patient to contact their physician. Testing revealed an elevated HbA1c (9.8%) and presence of glutamic acid decarboxylase autoantibodies (GAD) (9 IU/mL). Daily body composition measures and weighed food logs from the 3 months preceding and 4 months after diagnosis illustrate the effects of glucose spilling and inadequate insulin levels. Both FBG and PBG indicated diabetes 3 years prior to HbA1c. While FBG, PBG, and HbA1c are considered equally appropriate for screening and diagnosing diabetes, this case study highlights the need to revisit important distinctions between these tests that explain their frequent discordance.

The objective of this clinical practice guideline is to provide updated and new evidence-based recommendations for the comprehensive care of persons with diabetes mellitus to clinicians, diabetes-care teams, other health care professionals and stakeholders, and individuals with diabetes and their caregivers.

The American Association of Clinical Endocrinology selected a task force of medical experts and staff who updated and assessed clinical questions and recommendations from the prior 2015 version of this guideline and conducted literature searches for relevant scientific papers published from January 1, 2015, through May 15, 2022. Selected studies from results of literature searches composed the evidence base to update 2015 recommendations as well as to develop new recommendations based on review of clinical evidence, current practice, expertise, and consensus, according to established American Association of Clinical Endocrinology protocol for guideline development.

This guideline includes 170 updated and new evidence-based clinical practice recommendations for the comprehensive care of persons with diabetes. Recommendations are divided into four sections: (1) screening, diagnosis, glycemic targets, and glycemic monitoring; (2) comorbidities and complications, including obesity and management with lifestyle, nutrition, and bariatric surgery, hypertension, dyslipidemia, retinopathy, neuropathy, diabetic kidney disease, and cardiovascular disease; (3) management of prediabetes, type 2 diabetes with antihyperglycemic pharmacotherapy and glycemic targets, type 1 diabetes with insulin therapy, hypoglycemia, hospitalized persons, and women with diabetes in pregnancy; (4) education and new topics regarding diabetes and infertility, nutritional supplements, secondary diabetes, social determinants of health, and virtual care, as well as updated recommendations on cancer risk, nonpharmacologic components of pediatric care plans, depression, education and team approach, occupational risk, role of sleep medicine, and vaccinations in persons with diabetes.

This updated clinical practice guideline provides evidence-based recommendations to assist with person-centered, team-based clinical decision-making to improve the care of persons with diabetes mellitus.

In our previous published study, we demonstrated that a qualitatively assessed elevation in deltoid muscle echogenicity on ultrasound was both sensitive for and a strong predictor of a type 2 diabetes (T2DM) diagnosis. This study aims to evaluate if a sonographic quantitative assessment of the deltoid muscle can be used to detect T2DM.

Deltoid muscle ultrasound images from 124 patients were stored: 31 obese T2DM, 31 non-obese T2DM, 31 obese non-T2DM and 31 non-obese non-T2DM. Images were independently reviewed by 3 musculoskeletal radiologists, blinded to the patient's category. Each measured the grayscale pixel intensity of the deltoid muscle and humeral cortex to calculate a muscle/bone ratio for each patient. Following a 3-week delay, the 3 radiologists independently repeated measurements on a randomly selected 40 subjects. Ratios, age, gender, race, body mass index, insulin usage and hemoglobin A_1c were analyzed. The difference among the 4 groups was compared using analysis of variance or chi-square tests. Both univariate and multivariate linear mixed models were performed. Multivariate mixed-effects regression models were used, adjusting for demographic and clinical variables. Post hoc comparisons were done with Bonferroni adjustments to identify any differences between groups. The sample size achieved 90% power. Sensitivity and specificity were calculated based on set threshold ratios. Both intra- and inter-radiologist variability or agreement were assessed.

A statistically significant difference in muscle/bone ratios between the groups was identified with the average ratios as follows: obese T2DM, 0.54 (P < 0.001); non-obese T2DM, 0.48 (P < 0.001); obese non-T2DM, 0.42 (P = 0.03); and non-obese non-T2DM, 0.35. There was excellent inter-observer agreement (intraclass correlation coefficient 0.87) and excellent intra-observer agreements (intraclass correlation coefficient 0.92, 0.95 and 0.94). Using threshold ratios, the sensitivity for detecting T2DM was 80% (95% CI 67% to 88%) with a specificity of 63% (95% CI 50% to 75%).

The sonographic quantitative assessment of the deltoid muscle by ultrasound is sensitive and accurate for the detection of T2DM. Following further studies, this process could translate into a dedicated, simple and noninvasive screening method to detect T2DM with the prospects of identifying even a fraction of the undiagnosed persons worldwide. This could prove especially beneficial in screening of underserved and underrepresented communities.

Metabolic syndrome increases risk of complicating co-morbidities. Current clinical indicators reflect established metabolic impairment, preventing earlier intervention strategies. Here we show that circulating sphingolipids are altered in the very early stages of insulin resistance development. The study involved 16 paired overweight but healthy monkeys, one-half of which spontaneously developed metabolic syndrome over the course of 2 years. Importantly, animals did not differ in adiposity and were euglycemic throughout the study period. Using mass spectrometry, circulating sphingolipids, including ceramides and sphingomyelins, were detected and quantified for healthy and impaired animals at both time points. At time of diagnosis, several ceramides were significantly different between healthy and impaired animals. Correlation analysis revealed differences in the interactions among ceramides in impaired animals at diagnosis and pre-diagnosis when animals were clinically indistinguishable from controls. Furthermore, correlations between ceramides and early-stage markers of insulin resistance, diacylglycerols and non-esterified fatty acids, were distinct for healthy and impaired states. Regression analysis identifies coordinated changes in lipid handling across lipid classes as animals progress from healthy to insulin resistant. Correlations between ceramides and the adipose-derived adipokine adiponectin were apparent in healthy animals but not in the metabolically impaired animals, even in advance of loss in insulin sensitivity. These data suggest that circulating ceramides are clinically relevant in identifying disease risk independent of differences in adiposity, and may be important in devising preventative strategies.

We aimed to describe differences in the prevalence of intermediate hyperglycaemia (IH) between six ethnic groups. Moreover, to investigate differences in the association of the classifications of IH with the incidence of T2DM between ethnic groups.

We included 3759 Dutch, 2826 African Surinamese, 1646 Ghanaian, 2571 Turkish, 2691 Moroccan and 1970 South Asian Surinamese origin participants of the HELIUS study. IH was measured by fasting plasma glucose (FPG) and HbA1c. We calculated age-, BMI and physical-activity-adjusted prevalence of IH by sex, and calculated age and sex-adjusted hazard ratios (HR)for the association between IH and T2DM in each ethnic group.

The prevalence of IH was higher among ethnic minority groups (68.6-41.7%) than the Dutch majority (34.9%). The prevalence of IH categories varied across subgroups. Combined increased FPG and HbA1c was most prevalent in South-Asian Surinamese men (27.6%, 95 %CI: 24.5-30.9%), and in Dutch women (4.2%, 95 %CI: 3.4-5.1%). The HRs for T2DM for each IH-classification did not differ significantly between ethnic groups. HRs were highest for the combined classification, e.g., HR = 8.1, 95 %CI: 2.5-26.6 in the Dutch.

We found a higher prevalence of IH in ethnic minority versus majority groups, but did not find evidence for a differential association of IH with incident T2DM.

Dyslipidemia is a hallmark of diabetes mellitus (DM). However, specific lipid molecules closely associated with the initiation and progression of diabetes remain unclear. We used a pseudotargeted lipidomics approach to evaluate the complex lipid changes that occurred long before the diagnosis of type 2 diabetes mellitus (T2DM) and to identify novel lipid markers for screening prediabetes mellitus (PreDM) and T2DM in patients from multiple communities.

Four hundred and eighty-one subjects consisting of T2DM, three subtypes of PreDM, and normal controls (NC) were enrolled as discovery cohort. Serum lipidomic profiles of 481 subjects were analyzed using an ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-QqQ-MS)-based pseudotargeted lipidomics method. The differential lipid molecules were further validated in an independent case-control study consisting of 150 PreDM, 234 T2DM and 94 NC.

Multivariate discriminative analyses show that lipidomics data have considerable potential for identifying lipidome differences among T2DM, subtypes of PreDM and NC. Statistical associations of lipid (sub)species display significant variations in 11 lipid (sub)species levels for T2DM and distinctive differences in 8 lipid (sub)species levels between prediabetic and normoglycemic individuals, with further differences in 8 lipid (sub)species levels among subtypes of PreDM. Adjusted for sex, age and BMI, only two lipid (sub)species of fatty acid (FA) and phosphatidylcholine (PC) were associated at p< 0.05 for PreDM (all) and subtypes of PreDM. The defined lipid markers not only significantly improve the diagnostic accuracy of PreDM and T2DM but also effectively evaluating the risk of developing into each subtype of PreDM and T2DM when addition of age, sex, BMI, and FPG, respectively.

Our findings improve insights into the lipid metabolic complexity and interindividual variations among subtypes of PreDM and T2DM, beyond the well-known differences in dyslipidemia in clinic.

To investigate radiomics features of hepatic fat as potential biomarkers of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) in individuals without overt cardiovascular disease, and benchmarking against hepatic proton density fat fraction (PDFF) and the body mass index (BMI).

This study collected liver radiomics features of 310 individuals that were part of a case-controlled imaging substudy embedded in a prospective cohort. Individuals had known T2DM (n = 39; 12.6 %) and MetS (n = 107; 34.5 %) status, and were divided into stratified training (n = 232; 75 %) and validation (n = 78; 25 %) sets. Six hundred eighty-four MRI radiomics features were extracted for each liver volume of interest (VOI) on T₁-weighted dual-echo Dixon relative fat water content (rfwc) maps. Test-retest and inter-rater variance was simulated by additionally extracting radiomics features using noise augmented rfwc maps and deformed volume of interests. One hundred and seventy-one features with test-retest reliability (ICC(1,1)) and inter-rater agreement (ICC(3,k)) of ≥0.85 on the training set were considered stable. To construct predictive random forest (RF) models, stable features were filtered using univariate RF analysis followed by sequential forward aggregation. The predictive performance was evaluated on the independent validation set with area under the curve of the receiver operating characteristic (AUROC) and balanced accuracy (Accuracy_B).

On the validation set, the radiomics RF models predicted T2DM with AUROC of 0.835 and Accuracy_B of 0.822 and MetS with AUROC of 0.838 and Accuracy_B of 0.787, outperforming the RF models trained on the benchmark parameters PDFF and BMI.

Hepatic radiomics features may serve as potential imaging biomarkers for T2DM and MetS.

Hemoglobin A1C (A1C) is used in various settings. Its performance has not been evaluated systemically. We compared A1C in diagnosis of diabetes with fasting plasma glucose (FPG) and 2-h postchallenged plasma glucose (2hPG) parameters in a cross-sectional cohort in the United Stated. Adult subjects (≥20 years) were identified from the National Health and Nutrition Examination Survey 2005-2016 without a history of diabetes who had BMI, A1C, FPG, and 2hPG (n = 10,416). For comparisons, we calculated the sample weighted prevalence, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with subgroup analyses. For the retinopathy study, diabetic subjects with established diabetes who responded to the question of diabetic retinopathy were evaluated (n = 3907). Compared to the FPG/2hPG criteria, A1C ≥ 48 mmol/mol (6.5%) had a low sensitivity at 25.90%, with specificity 99.70%, PPV 84.70%, and NPV 95.70%. Subgroup analyses revealed a lower sensitivity in males (24.52%); the lowest in non-Hispanic White (21.35%), in the third decade (14.32%), and in the BMI < 22.50 kg/m2 group (7.21%). The prevalence of self-reported diabetic retinopathy increased drastically with an inflection point at A1C 48 mmol/mol (6.5%) from 11.52% to 18.32% (p < 0.0001). A1C ≥ 48 mmol/mol (6.5%) should be cautiously used to diagnose diabetes in certain subgroups due to very low sensitivity in certain groups. With the confirmation of the association of increasing self-reported diabetic retinopathy with A1C ≥ 48 mmol/mol (6.5%), the current A1C cutoff is an acceptable value with the understanding of especially low sensitivity in certain subgroups.

Diabetes is a well-established risk factor for dementia, but its impact on the prodromal phase of dementia is unclear.

Cohorts of older adults who were cognitively healthy (n = 1840) or had cognitive impairment-no dementia (CIND; n = 682) were followed over 12 years to detect incident CIND and dementia, respectively.

Poorly controlled diabetes (glycated hemoglobin [HbA1c] ≥7.5%; reference = normoglycemia) was associated with double the risk of CIND (Cox regression multi-adjusted hazard ratio [HR] 2.01, 95% confidence interval [CI] 1.13-3.58) and triple the risk CIND progressing to dementia (HR 2.87, 95% CI 1.20-6.85). Co-morbid diabetes and heart disease doubled the risk of incident CIND and dementia, although neither disease conferred a significant risk of either outcome alone. Elevated systemic inflammation contributed to the diabetes-associated increased dementia risk.

Diabetes characterized by poor glycemic control or cardiovascular complications is related to a greater risk of the development and progression of cognitive impairment. Inflammation may play a role in these relationships.

There is strong evidence that diabetes is underdiagnosed in the US: the Centers for Disease Control and Prevention (CDC) estimates that approximately 25% of diabetic patients are unaware of their condition. To encourage timely diagnosis of at-risk patients, we develop screening guidelines stratified by body mass index (BMI), age, and prior test history by using a Partially Observed Markov Decision Process (POMDP) framework to provide more personalized screening frequency recommendations. We identify structural results that prove the existence of threshold solutions in our problem and allow us to determine the relative timing and frequency of screening given different risk profiles. We then use nationally representative empirical data to identify a policy that provides the optimal action (screen or wait) every six months from age 45 to 90. We find that the current screening guidelines are suboptimal, and the recommended diabetes screening policy should be stratified by age and by finer BMI thresholds than in the status quo. We identify age ranges and BMI categories for which relatively less or more screening is needed compared to the existing guidelines to help physicians target patients most at risk. Compared to the status quo, we estimate that an optimal screening policy would generate higher net monetary benefits by $3,200-$3,570 and save $120-$1,290 in health expenditures per individual in the US above age 45.

This study aimed to find a potential biomarker that can be used to diagnose prediabetic condition by comparing the salivary bacterial microbiomes between Thai dental patients with normoglycemia (NG) and those with potential prediabetes (PPG) conditions. Thirty-three subjects were randomly recruited. Demographic data were collected along with oral examination and unstimulated salivary collections. The salivary bacterial microbiomes were identified by high-throughput sequencing on the V3-V4 region of the bacterial 16S rRNA gene. Microbiomes in this study were composed of 12 phyla, 19 classes, 29 orders, 56 families, 81 genera, and 184 species. To check the validity of the selection criterion for prediabetes, we adopted two separate criteria to divide samples into PPG and NG groups using glycated hemoglobin A1c (HbA1c) or fasting plasma glucose (FPG) levels. Using the HbA1c level resulted in the significant reduction of Alloprevotella, Neisseria, Rothia, and Streptococcus abundances in PPG compared with those in NG (p-value < 0.05). On the other hand, the abundance of Absconditabacteriales was significantly reduced whereas Leptotrichia, Stomatobaculum, and Ruminococcaceae increased in the PPG group when the samples were classified by the FPG level (p-value < 0.05). It is implied that the group classifying criterion should be carefully concerned when investigating relative abundances between groups. However, regardless of the criteria, Rothia is significantly dominant in the NG groups, suggesting that Rothia might be a potential prediabetic biomarker. Due to the small sample size of this study, further investigation with a larger sample size is necessary to ensure that Rothia can be a potential biomarker for prediabetes in Thai people.