Primary care diabetes最新文献

Background: Type 2 Diabetes Mellitus (T2DM) requires a multifaceted management approach involving lifestyle modifications, education, and pharmacological treatments. Medication adherence is critical for achieving glycaemic control; however, up to 45 % of patients fail to meet HbA1c targets. The Morisky Medication Adherence Scale (MMAS) is a validated tool widely used in clinical practice to assess self-reported medication adherence, offering valuable insights into patient behaviours affecting treatment outcomes.

Aim: This systematic review evaluates medication adherence in adults with T2DM using the MMAS and to identify modifiable factors influencing non-adherence. The COM-B model was used to structure the analysis by mapping barriers and enablers to the Capability, Opportunity, and Motivation components that underpin medication-taking Behaviour.

Method: A systematic review was conducted following the PRISMA framework. MEDLINE, EMBASE, EMCARE, and Ovid Nursing databases were searched for cross-sectional studies published between January 2013 and December 2024 that utilised the MMAS scale to assess adherence in adults with T2DM receiving oral or injectable anti-glycaemic therapies. A narrative synthesis was conducted using the COM-B model to identify key barriers and enablers influencing adherence.

Results: Of 9990 records screened, 30 studies from 17 countries, involving 8405 participants, met the inclusion criteria. Overall, 40.9 % of participants demonstrated high adherence, while 42.6 % had low adherence. Key barriers included poor diabetes knowledge, depression, polypharmacy, side effects, inadequate patient-provider communication, and lack of continuity in care. Enablers encompassed patient education, family support, effective patient-provider communication, and structured diabetes education programmes.

Conclusions: The MMAS remains a reliable tool for assessing self-reported medication adherence in T2DM. However, adherence levels remain suboptimal. Addressing modifiable factors, such as depression, enhancing diabetes education, and improving healthcare communication, may improve adherence, glycaemic control, and overall T2DM management outcomes.

Registration: Registered with PROSPERO (CRD42022359969).

Aims: Type 2 diabetes mellitus (T2DM) is a growing public‑health challenge in Greece, yet validated tools to assess care quality in primary settings are lacking. We aimed to develop a context‑specific set of consensus‑based quality indicators (QIs) for T2DM in Greek primary care, with an eye toward broader applicability in comparable European and low‑ and middle‑income country (LMIC) systems.

Methods: A two-phase study was conducted: (1) a focused bibliographic search (Medline/PubMed and Scopus; January 2019-August 2020) to identify existing T2DM QI sets suitable for primary care; and (2) a three-round modified Delphi survey with 10 stakeholders (general practitioners, diabetologist, dietician, health policy expert, patients) to evaluate indicator importance and feasibility and reach consensus. Candidate indicators were contextually adapted for European relevance prior to Delphi rating.

Results: The bibliographic search identified 16 records; only one study met eligibility criteria and provided 43 candidate QIs. The Delphi process yielded 39 QIs across nine domains: access (2), monitoring (13), health counseling (3), treatment and prevention (2), patient safety (3), records (1), health status (11), patient satisfaction (2), and self-management (2).

Conclusions: We present the first context-adapted, consensus-based T2DM QI set for Greek primary care, covering clinical, organizational, and patient-reported aspects of care. This framework lays the groundwork for wider implementation, electronic health record integration, and international benchmarking.

Background: We aimed to explore the prevalence of and clinical factors that identify obstructive sleep apnoea (OSA) in older adults with suboptimally-controlled type 2 diabetes (T2D) and comorbid insomnia.

Methods: In this cross-sectional analysis, participants had age≥ 60 years, HbA_1c7.5-10 % and comorbid insomnia [insomnia severity index (ISI)> 14] with no prior history of OSA. All participants had home sleep apnoea testing done to measure apnoea-hypopnea index (AHI) and completed questionnaires including self-reporting of habitual snoring (≥3 nights/week), Four-Variable Screening Tool (FVST) comprising sex, body mass index (BMI), blood pressure and frequency of snoring, and excessive daytime sleepiness (EDS) [Epworth Sleepiness Scale (ESS) score ≥ 10]. Mild, moderate, and severe OSA were defined by AHI 5 to < 15, ≥ 15 to < 30, and ≥ 30 events/hour respectively.

Results: Among 93 participants(mean age: 68.3 ± 4.8 years, 46 % men, BMI:24.8 ± 3.7 kg/m²), 87 % had OSA (43 % moderate-to-severe OSA) and 41.8 % had EDS. A diagnosis of OSA was associated with BMI[odd ratio(OR)1.233(95 %CI:1.015-1.498),p = 0.035], habitual snoring[8.107(2.192-29.977), p = 0.002], and FVST[1.386(1.132-1.698), p = 0.002]. The cut-off value of FVST≥ 5 or ESS≥ 10 had 100 % sensitivity and 20.8 (9.8-31.7)% specificity to detect moderate-to-severe OSA.

Conclusions: Older adults with suboptimally-controlled T2D and comorbid insomnia had high prevalence of undiagnosed OSA with FVST and EDS as potentially useful screening tools.

Aim: To compare osteoporosis risk associated with sodium-glucose cotransporter 2 inhibitor (SGLT2i) versus dipeptidyl peptidase-4 inhibitor (DPP-4i) in older women with type 2 diabetes (T2D).

Methods: This cohort study used the Korean National Health Insurance database. Adjusted hazard ratios (aHRs) were estimated using multivariable Cox regression. Sensitivity analysis was performed using inverse probability of treatment weighting (IPTW).

Results: Among 1715,337 women aged ≥ 55 years with T2D, 5364 SGLT2i users and 26,504 DPP-4i users were analyzed. The incidence rates of osteoporosis were 6.7 (95 % CI, 6.0-7.4) and 7.0 (95 % CI, 6.7-7.3) per 100 person-years in the SGLT2i and DPP-4i groups, respectively. Osteoporosis risk with SGLT2i was comparable to DPP-4i (aHR, 0.93; 95 % CI, 0.89-1.04). These results remained consistent in the sensitivity analysis using IPTW (aHR, 0.95; 95 % CI, 0.89-1.00). Subgroup analyses showed a slightly lower osteoporosis risk with SGLT2i in patients aged 55-64 years (aHR, 0.84; 95 % CI, 0.76-0.93), those without prior rheumatoid arthritis (aHR, 0.90; 95 % CI, 0.83-0.97), proton pump inhibitor use (aHR, 0.88; 95 % CI, 0.80-0.96), or oral corticosteroid use (aHRs, 0.91; 95 % CI, 0.85-0.97).

Conclusion: SGLT2i was comparable to DPP-4i in osteoporosis risk among older women with T2D.

We determined whether achieving glycemic control during pregnancy was associated with fewer postpartum depressive symptoms among individuals with pregestational diabetes. We found achievement of pregnancy glycemic targets was not associated with a lower risk of postpartum depressive symptoms in a population with a high frequency of pre-existing psychiatric morbidity.

Aims: Fetal and neonatal complications are common in pregnancies affected by maternal type 1 diabetes. The aim of this study is to investigate whether type 1 diabetes during pregnancy is reflected in offspring long-term mortality during a follow-up of up to 30 years.

Methods: This register-based follow-up study included 1762 offspring of women with type 1 diabetes (cases) and 8810 offspring of women without diabetes (controls). Time and causes of death between 1988 and 2018 were obtained from Statistics Finland. Clinical characteristics at birth of study participants and their mothers were obtained from the Finnish Medical Birth Register.

Results: Cases had an 1,67 times higher mortality than controls during the 30-year follow-up period (crude HR 1.67 [95 % CI: 1.01-2.77]), with a median follow-up of 21 years (IQR 15-28). Cases possessed an adverse risk-profile at the neonatal stage. Endocrine, nutritional and metabolic diseases (IRR 5.02/100 000 person years [95 % CI 1.01-24.88]), and diseases of the nervous system (IRR 5.03/100 000 person years [95 % CI 1.16-21.82]) were overrepresented as the cause of death in this group.

Conclusions: These findings suggest that maternal type 1 diabetes during pregnancy is associated with higher mortality during a 30-year follow-up.

Aim: Glycated hemoglobin (HbA_1c) is a key indicator of diabetes control. However, the risk of missing HbA_1c values in clinical records is unknown. We aimed to analyze the relationship between missing HbA_1c values and the occurrence of major cardiovascular events or death from all causes.

Methods: Retrospective cohort study based on a national database of primary care electronic medical records in Spain (BIFAP). We included people aged 30 years and older with an incident diagnosis of diabetes mellitus. Follow-up started on the date of diabetes diagnosis between 2005 and 2019, and ended on occurrence of composite endpoint (major cardiovascular events and/or death from all causes), or December 31st, 2019. The baseline exposure variable was HbA_1c (< 7 %, 7 %-8 %, > 8 %, missing). Cox models were fitted.

Results: Our analysis included 303,199 people with diabetes, with an average age of 62.2 years and 44.7 % were women. The mean follow-up was 5.7 years, and 10.2 % of patients had missing HbA_1c values. The cardiovascular risk was 1.18 (95 %CI: 1.14-1.22) in the HbA_1c 7 %-8 % group, 1.41 (95 %CI: 1.36-1.46) in HbA_1c > 8 %, and 2.95 (95 %CI: 2.89-3.05) in HbA_1c missing, compared with HbA_1c < 7 %.

Conclusions: In this large cohort of people with newly diagnosed diabetes, missing HbA1c values was associated with a significantly higher risk of major cardiovascular events or death, more than double the risk observed in people with the worst glycemic control. These findings underscore the clinical importance of routinely recording and monitoring HbA1c at diagnosis, not only as a marker of metabolic control but also as a potential prognostic indicator. The lack of HbA1c may act as an indicator of suboptimal clinical follow-up.

Objective: This study evaluated the impact of real-time continuous glucose monitoring (CGM) combined with personalized digital health coaching on glycemic control and lifestyle behaviors in individuals with type 2 diabetes (T2DM) and prediabetes.

Method: A prospective cohort study was conducted involving 110 participants recruited from a chronic disease management service. The participants underwent an 8-week intervention where CGM data were used to provide real-time feedback, complemented by personalized lifestyle coaching. Baseline and post-intervention data included fasting blood glucose (FBG), glycated hemoglobin (HbA1c), body mass index (BMI), and lifestyle factors such as physical activity and eating habits. Participants were divided into tertiles based on mean amplitude of glycemic excursion (MAGE) to evaluate the effects of the intervention by glycemic variability (GV) level.

Results: Significant improvements in glycemic control were observed across all tertiles. The highest GV group (T3) showed the greatest reductions in HbA1c (7.39 % to 6.82 %, p = 0.004) and FBG. The physical activity scores significantly increased in the T3 group (p = 0.005), and all tertiles reported healthier dietary habits following the intervention.

Conclusions: The integration of CGM with personalized digital health coaching was associated with significant short-term improvements in glycemic control and lifestyle behaviors, particularly among individuals with high GV.

Aim: Research on the epidemiology of diabetes mellitus (DM) has identified a geographically distinct region in the United States (U.S.) known as the diabetes belt (DM Belt), which represents a significant public health concern. This study aimed to examine the factors contributing to the increased risk of DM in the DM Belt compared to the non-DM Belt.

Methods: Data were analyzed from 398,243 adults aged ≥ 18 years who participated in the 2019 Behavior Risk Factor Surveillance System. DM status was based on participants' self-reported physician-diagnosed DM. The DM Belt was defined at the state level according to the U.S. Center for Disease Control and Prevention's classification. Logistic regression (LR) was used to estimate odds ratios for DM and assess the excess DM risk in the DM Belt versus the non-DM Belt. Random Forest (RF) and stepwise LR were employed to identify and rank key contributors to the excess DM risk.

Results: Residents of the DM Belt had a significantly higher prevalence of DM than those in the non-DM Belt (age-sex-adjusted rate: 12.5 % versus 10.5 %, p < 0.001). Low socioeconomic status (SES), physical inactivity, and hypertension were identified as the top three factors explaining the excess DM risk in the DM Belt.

Conclusions: These findings underscore the importance of an integrated approach to improving SES, promoting healthy behaviors, managing chronic conditions for reducing DM risk. Addressing these factors can help mitigate health disparities in DM risk across the U.S.

This commentary provides methodological reflections on a recent multi-country study investigating insulin resistance (IR) marker heterogeneity in adults newly diagnosed with diabetes across 14 low and middle-income countries (LMICs). While commending the significant contribution of this research, we highlight three key considerations to refine the interpretation of findings and guide future studies. First, we discuss the need for population-specific validation of non-insulin-based IR markers, as their performance varies substantially across ethnic groups. Second, we examine the implications of relying on a single fasting glucose measurement for diabetes diagnosis, which may impact phenotypic characterization and subtype differentiation. Finally, we explore the importance of quantifying the relative contributions of multi-level factors-including adiposity, lifestyle, and healthcare system variables-to better understand the observed cross-country heterogeneity. Addressing these methodological aspects in future research will strengthen the evidence base for developing tailored diabetes care strategies in diverse global populations.