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In response to a study previously published in PLOS Biology, this Formal Comment thoroughly examines the concept of 'glucotypes' with regard to its generalisability, interpretability and relationship to more traditional measures used to describe data from continuous glucose monitoring.

Non-alcoholic fatty liver disease (NAFLD) is highly prevalent among individuals with type 2 diabetes. Although epidemiological studies have shown that NAFLD is associated with cardiovascular disease (CVD), it remains unknown whether NAFLD is an active contributor or an innocent bystander. Plasma lipids, low-grade inflammation, impaired fibrinolysis and hepatokines are potential mediators of the relationship between NAFLD and CVD. The Mendelian randomisation approach can help to make causal inferences. Studies that used common variants in PNPLA3, TM6SF2 and GCKR as instruments to investigate the relationship between NAFLD and coronary artery disease (CAD) have reported contrasting results. Variants in PNPLA3 and TM6SF2 were found to protect against CAD, whereas variants in GCKR were positively associated with CAD. Since all three genes have been associated with non-alcoholic steatohepatitis, the second stage of NAFLD, the question of whether low-grade inflammation is an important mediator of the relationship between NAFLD and CAD arises. In contrast, the differential effects of these genes on plasma lipids (i.e. lipid-lowering for PNPLA3 and TM6SF2, and lipid-raising for GCKR) strongly suggest that plasma lipids account for their differential effects on CAD risk. This concept has recently been confirmed in an extended set of 12 NAFLD susceptibility genes. From these studies it appears that plasma lipids are an important mediator between NAFLD and CVD risk. These findings have important clinical implications, particularly for the design of anti-NAFLD drugs that also affect lipid metabolism.

Small-molecules that disrupt the binding between glucokinase and glucokinase regulatory protein (GKRP) in the liver represent a potential new class of glucose-lowering drugs. It will, however, take years before their effects on clinically relevant cardiovascular endpoints are known. The purpose of this study was to estimate the effects of these drugs on cardiorenal outcomes by studying variants in the GKRP gene (GCKR) that mimic glucokinase-GKRP disruptors. The MEDLINE and EMBASE databases were searched for studies reporting on the association between GCKR variants (rs1260326, rs780094, and rs780093) and coronary artery disease (CAD), estimated glomerular filtration rate (eGFR), and chronic kidney disease (CKD). In total 5 CAD studies (n = 274,625 individuals), 7 eGFR studies (n = 195,195 individuals), and 4 CKD studies (n = 31,642 cases and n = 408,432 controls) were included. Meta-analysis revealed a significant association between GCKR variants and CAD (OR:1.02 per risk allele, 95%CI:1.00-1.04, p = 0.01). Sensitivity analyses showed that replacement of one large, influential CAD study by two other, partly overlapping studies resulted in similar point estimates, albeit less precise (OR:1.02; 95%CI:0.98-1.06 and OR: 1.02; 95%CI: 0.99-1.04). GCKR was associated with an improved eGFR (+0.49 ml/min, 95%CI:0.10-0.89, p = 0.01) and a trend towards protection from CKD (OR:0.98, 95%CI:0.95-1.01, p = 0.13). This study suggests that increased glucokinase-GKRP disruption has beneficial effects on eGFR, but these may be offset by a disadvantageous effect on coronary artery disease risk. Further studies are warranted to elucidate the mechanistic link between hepatic glucose metabolism and eGFR.

Background Patient decision aids (PDAs) can effectively facilitate shared decision-making (SDM) between patients and healthcare professionals. The International Patient Decision Aid Standards (IPDAS) Collaboration created a model for the systematic development of PDAs. However, the absence of a solid evidence base limits practical recommendations for best practices. For example, uncertainties exist about the most appropriate method for identifying the needs and preferences of patients and healthcare professionals. This study aims to detail the development process for the development of a PDA for type 2 diabetes mellitus (T2DM), using the IPDAS model. Methods From September 2020 to February 2023, we systematically developed the PDA for T2DM in the Netherlands. We adopted a patient-centered approach by researching patient considerations and actively collaborating with a multidisciplinary steering group, including patients with T2DM, patient organizations, and healthcare professionals. The PDA content and prototype development were determined by incorporating patients’ needs and preferences, input from the steering group, and available evidence regarding T2DM treatment options. The research team and steering group iteratively reviewed the PDA prototype. Results A web-based PDA was developed consisting of five sections: 1) information about T2DM and the available treatment options; 2) comparison of treatment options; 3) questions to assess patients’ knowledge; 4) value-clarification exercise; and 5) summary of the patient’s journey through the PDA. Before patients use the PDA, healthcare professionals can preselect the most relevant treatment options. Conclusions Early and iterative involvement of relevant stakeholders in the development process of the PDA helped the alignment of the PDA with the needs and preferences of the diverse end-users. In a future study, we will investigate the effectiveness of the PDA in facilitating SDM in T2DM care. Trial registration International Clinical Trials Registry Platform ID: NL8948, date of registration: 05–10-2020.

We developed a multistage shared decision-making program for type 2 diabetes that aims to support person-centered type 2 diabetes management in primary care. The program consists of an online patient decision aid, a preparatory consult for patients, and interprofessional training for healthcare professionals. The short- and long-term effectiveness of the multistage shared decision-making program needs to be researched in a trial-based economic evaluation. To evaluate the feasibility of study methods for such an evaluation, we will conduct a pilot study. In this article, we outline its protocol, which focuses on sample recruitment and retention, study management, and feasibility of outcome and cost measurements. The multistage shared decision-making program will be pilot-tested in a cluster-randomized controlled trial in four primary care practices (located in the region of Gorinchem, the Netherlands) using a convergent parallel mixed-methods approach. The intervention practices will adopt the program, whereas the control practices provide usual care. Data collection will include recruitment, retention, and consent rates, patients' sociodemographic and clinical characteristics, and the assessment of primary and secondary outcomes of the future trial-based economic evaluation. We will also collect data on the usage behavior of patients when completing questionnaires of the primary and secondary outcomes (i.e., time needed to complete questionnaires). Semi-structured interviews with patients will be conducted to obtain insights into the understandability and usability of measurement tools. Moreover, focus groups with healthcare professionals from participating practices will be organized to complement the quantitative data on sample representativeness and to assess the study management challenges of participating practices. The pilot will address uncertainties around the feasibility of a future trial-based economic evaluation, focusing on sample recruitment and retention, study management, and the feasibility of outcome and cost measurements. The results will guide the improvement of study procedures for the economic evaluation of our multistage shared decision-making program for type 2 diabetes. The study is registered at ClinicalTrials.gov: NCT06410768.

Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. We used data from The Maastricht Study, an observational population-based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman's correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

Background Recently, there has been growing interest in providing more tailored, patient-centered care for the treatment of type 2 diabetes mellitus (T2DM). Yet it remains unclear which patient characteristics should be determined to guide such an approach. Therefore, the opinions of healthcare providers (HCP) and people with T2DM about relevant patient characteristics for estimating healthcare needs of people with T2DM were assessed and compared. Methods Two separate online Delphi studies were conducted according to the RAND-UCLA Appropriateness Method: one with HCPs ( n  = 22) from Dutch primary and secondary care and one with people with T2DM treated in Dutch primary care ( n  = 46). The relevance of patient characteristics for estimating healthcare needs, defined as the number of yearly consultations, was assessed on a 5-point Likert scale. Characteristics with a median of 4 or 5 and an interquartile range ≤ 1.5 were considered relevant with consensus. Participants were also asked to select the top 5 of most relevant patient characteristics. To determine the overall top 5, the mean relative importance score of each characteristic was calculated. Results In two Delphi rounds, 28 and 15 patient characteristics were rated by HCPs and people with T2DM, respectively. Both HCPs and people with T2DM found health-related characteristics relevant for estimating healthcare needs of people with T2DM. However, HCPs preferred to estimate healthcare needs using person- and context-related characteristics. They ranked self-efficacy as the most relevant estimator. In contrast, people with T2DM were more in favor of health-related characteristics and ranked HbA1c as the most relevant estimator. Conclusions The findings show that there is discrepancy in opinions on relevant patient characteristics for estimating healthcare needs between HCPs and people with T2DM. To achieve more tailored, patient-centered care, it is important that both groups agree on the topics to be discussed during patient consultations.

Hepatic steatosis is associated with poor cardiometabolic health, with de novo lipogenesis (DNL) contributing to hepatic steatosis and subsequent insulin resistance. Hepatic saturated fatty acids (SFA) may be a marker of DNL and are suggested to be most detrimental in contributing to insulin resistance. Here, we show in a cross-sectional study design (ClinicalTrials.gov ID: NCT03211299) that we are able to distinguish the fractions of hepatic SFA, mono- and polyunsaturated fatty acids in healthy and metabolically compromised volunteers using proton magnetic resonance spectroscopy ( 1 H-MRS). DNL is positively associated with SFA fraction and is elevated in patients with non-alcoholic fatty liver and type 2 diabetes. Intriguingly, SFA fraction shows a strong, negative correlation with hepatic insulin sensitivity. Our results show that the hepatic lipid composition, as determined by our 1 H-MRS methodology, is a measure of DNL and suggest that specifically the SFA fraction may hamper hepatic insulin sensitivity. Hepatic steatosis is associated with poor cardiometabolic health, with de novo lipogenesis (DNL) contributing to hepatic steatosis and subsequent insulin resistance. Here, the authors use 1 H-MRS methodology to show hepatic SFA fraction is a measure of DNL and specifically may hamper hepatic insulin sensitivity.

Abstract Context There is an ongoing debate about whether and how fructose is involved in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). A recent experimental study showed an increased intrahepatic triglyceride (IHTG) content in mice deficient for aldolase B (aldo B−/−), the enzyme that converts fructose-1-phosphate to triose phosphates. Objective To translate these experimental findings to the human situation. Design Case-control study. Setting Outpatient clinic for inborn errors of metabolism. Patients or Other Participants Patients with hereditary fructose intolerance, a rare inborn error of metabolism caused by a defect in aldolase B (n = 15), and healthy persons matched for age, sex, and body mass index (BMI) (n =15). Main Outcome Measure IHTG content, assessed by proton magnetic resonance spectroscopy. Results IHTG content was higher in aldo B−/− patients than controls (2.5% vs 0.6%; P = 0.001) on a background of lean body mass (median BMI, 20.4 and 21.8 kg/m2, respectively). Glucose excursions during an oral glucose load were higher in aldo B−/− patients (P = 0.043). Hypoglycosylated transferrin, a surrogate marker for hepatic fructose-1-phosphate concentrations, was more abundant in aldo B−/− patients than in controls (P < 0.001). Finally, plasma β-hydroxybutyrate, a biomarker of hepatic β-oxidation, was lower in aldo B−/− patients than controls (P = 0.009). Conclusions This study extends previous experimental findings by demonstrating that aldolase B deficiency also results in IHTG accumulation in humans. It suggests that the accumulation of fructose-1-phosphate and impairment of β-oxidation are involved in the pathogenesis. Patients with aldolase B deficiency, a defect in fructose metabolism, are characterized by increased hepatic fat content and glucose intolerance compared with age-, sex-, and BMI-matched controls.

AimsCVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis.MethodsWe included participants of The Maastricht Study, an observational population-based cohort, who underwent at least 48 h of continuous glucose monitoring (CGM) (n = 853; age: 59.9 ± 8.6 years; 49% women, 23% type 2 diabetes). We studied the cross-sectional associations of two glucose variability indices (CGM-assessed SD [SDCGM] and CGM-assessed CV [CVCGM]) and time in range (TIRCGM) with carotid–femoral pulse wave velocity (cf-PWV), carotid distensibility coefficient, carotid intima–media thickness, ankle–brachial index and circumferential wall stress via multiple linear regression.ResultsHigher SDCGM was associated with higher cf-PWV after adjusting for demographics, cardiovascular risk factors and lifestyle factors (regression coefficient [B] per 1 mmol/l SDCGM [and corresponding 95% CI]: 0.413 m/s [0.147, 0.679], p = 0.002). In the model additionally adjusted for CGM-assessed mean sensor glucose (MSGCGM), SDCGM and MSGCGM contributed similarly to cf-PWV (respective standardised regression coefficients [st.βs] and 95% CIs of 0.065 [−0.018, 0.167], p = 0.160; and 0.059 [−0.043, 0.164], p = 0.272). In the fully adjusted models, both higher CVCGM (B [95% CI] per 10% CVCGM: 0.303 m/s [0.046, 0.559], p = 0.021) and lower TIRCGM (B [95% CI] per 10% TIRCGM: −0.145 m/s [−0.252, −0.038] p = 0.008) were statistically significantly associated with higher cf-PWV. Such consistent associations were not observed for the other arterial measures.ConclusionsOur findings show that greater daily glucose variability and lower TIRCGM are associated with greater aortic stiffness (cf-PWV) but not with other arterial measures. If corroborated in prospective studies, these results support the development of therapeutic agents that target both daily glucose variability and TIRCGM to prevent CVD.