The prevalence of diabetes mellitus is increasing globally. In 2021, 537 million people have diabetes, and this number will be 643 million by 2030 and 783 million by 2045. In 2021, more than 6.7 million people aged 20–79 died from diabetes-related causes. The number of children and adolescents (up to 19 years old) living with diabetes continues to rise each year. In 2021, it was estimated that over 1.2 million children and adolescents were living with type 1 diabetes. Direct healthcare costs related to diabetes are extremely high, reaching approximately one trillion USD in 2021, and are expected to surpass this figure by 2030.

Background/Objectives: Metabolic syndrome (MetS), defined by abdominal obesity, dysglycemia, dyslipidemia, hypertension, and insulin resistance, markedly increases the risk of type 2 diabetes mellitus and cardiovascular disease. Affecting an estimated 25–30% of the global adult population, MetS represents a major and growing public health challenge. A growing body of evidence supports a significant bidirectional relationship between MetS and oral health, particularly periodontitis. The present study aimed to synthesize current evidence on the pathophysiological mechanisms, epidemiological associations, interventional outcomes, and clinical implications of the bidirectional relationship between metabolic syndrome (MetS) and periodontitis. Methods: A narrative review following the SANRA framework was performed. PubMed, Scopus, and Web of Science were searched for articles published in January 2021–March 2026 using MeSH and free-text terms including “metabolic syndrome”, “periodontal disease”, “insulin resistance”, and “oral microbiota”. Eligible studies included original research and systematic reviews in English with full-text availability; animal and in vitro studies were included if directly informative of mechanistic pathways. Results: A total of 64 references were selected for inclusion. Shared mechanisms include chronic systemic inflammation, insulin resistance, oxidative stress, adipokine imbalance, endothelial dysfunction, and oral–gut microbiome dysbiosis. Cross-sectional and longitudinal studies show that MetS components are independently associated with higher prevalence and severity of periodontitis; meta-analyses report pooled odds ratios of 1.7–1.9 compared with metabolically healthy controls. Non-surgical periodontal therapy produces modest but significant reductions in glycated hemoglobin (HbA1c) and systemic inflammatory markers. Sodium–glucose cotransporter-2 (SGLT2) inhibitors may alter oral microbiota composition and cause mucosal changes, while glucagon-like peptide-1 (GLP-1) receptor agonists may increase caries risk through gastrointestinal side effects and xerostomia; both drug classes warrant proactive dental monitoring. Conclusions: The bidirectional relationship between MetS and oral health supports integrated screening and interdisciplinary management. Routine periodontal assessment should be integrated into the metabolic risk management pathway, and dental professionals should screen patients with severe periodontitis for metabolic risk factors. The oral microbiome emerges as a promising target for future mechanistic research and therapeutic intervention. Recognition of oral health as an integral component of metabolic health may improve risk stratification, prevention, and long-term patient outcomes. Large-scale randomized controlled trials with standardized endpoints are needed to establish causal directionality and optimize combined therapeutic strategies.

Background and Objectives: Gestational diabetes mellitus (GDM) is a complex condition characterized by metabolic disorders of blood glucose that significantly impact the health of both mother and fetus. The objectives of this study were to assess the prevalence and risk factors for maternal and fetal–neonatal complications in women with GDM, comparing them to a control group (pregnant women without GDM) and pregnant women with type 1 diabetes mellitus (T1DM) or type 2 diabetes (T2DM). Materials and Methods: A retrospective observational study was conducted with 1418 pregnant women (279 with GDM, 74 with T1DM, 107 with T2DM, and 958 in the control group). The retrospective data included information on demographics, diagnostic test results, the medical history of pregnant women, treatments administered, identified complications, and other relevant variables for the study’s purpose. Results: Significant differences were found regarding maternal and neo-fetal complications between GDM and the control group in terms of abortion, pregnancy-induced hypertension, and increased fetal weight (macrosomia). Women with T1DM and T2DM showed a higher rate of abortion, premature birth, and an APGAR score of <7 at 5 min compared to those with GDM, and for T1DM, there was a higher rate of fetal mortality than in GDM cases. The primary risk factors for maternal complications included age OR = 1.03 (95% CI: 1.01–1.05, p = 0.002), obesity OR = 2.37 (95% CI: 1.42–3.94, p < 0.001), and chronic hypertension OR = 2.51 (95% CI: 1.26–5.01, p = 0.009). Age and obesity were also significant cofactors for maternal complications. Furthermore, the main significant risk factors for fetal–neonatal complications were obesity OR = 2.481 (95% CI:1.49–4.12, p < 0.001) and chronic hypertension OR = 2.813 (95% CI:1.44–5.49, p = 0.002), both independently and as cofactors. Conclusions: We found that obesity and chronic hypertension are risk factors for both maternal and fetal–neonatal complications. It is essential to prevent and adequately treat these two factors among pregnant women to avoid the onset of GDM. Additionally, screening for GDM is necessary to prevent maternal and fetal complications. Our results highlight the importance of specialized medical care and tailored management protocols in mitigating risks and ensuring positive outcomes for both mother and child during and after childbirth.

Background: Metabolic dysfunction is a complex process arising from coordinated interactions among insulin resistance, dyslipidemia, hepatic dysfunction, obesity, low-grade inflammation, and endocrine alterations. The triglyceride–glucose (TyG) index is a simple, reproducible surrogate of insulin resistance, but most studies have evaluated it through isolated association-based analyses. We characterized the endocrine–metabolic architecture associated with TyG in patients with thyroid dysfunction using a systems-phenotyping approach. Methods: In this retrospective, single-center, cross-sectional study of 387 adults evaluated for thyroid dysfunction, unsupervised phenotypes were derived by principal component analysis (PCA), K-means, Gaussian mixture models (GMM), and hierarchical (Ward) clustering using fully measured primary metabolic variables (fasting glucose, triglycerides, HDL- and LDL-cholesterol, body mass index, alanine, and aspartate aminotransferase). TyG was then examined as a descriptor of the identified architecture. Model-based latent profile analysis (BIC, entropy) and a censoring-aware, rank-based Gaussian graphical model (GGM) of partial correlations were additionally estimated. Results: Three overlapping, clinically interpretable phenotypic partitions of an underlying metabolic continuum were identified: a metabolically preserved phenotype (MP), an intermediate hepatic-dominant phenotype (IHD), and a severe insulin-resistance-dominant phenotype (SIRD), characterized by obesity, atherogenic dyslipidemia, and hyperuricemia. TyG values increased monotonically across the three partitions (medians 8.24, 8.74, and 8.87 in MP, IHD, and SIRD, respectively; p < 0.001), providing an accessible single-number summary of the metabolic gradient. Cluster separation was modest (silhouette = 0.17), consistent with a continuum, but the three-profile solution was supported by latent profile analysis (ΔBIC = −96.7; relative entropy = 0.75) and was reproduced across frameworks (adjusted Rand index of 0.75 with GMM and 0.46 with Ward). In the censoring-aware GGM, metabolic and thyroid variables formed two near-orthogonal blocks (maximum absolute metabolic–thyroid partial correlation: 0.16; median: 0.05). Conclusions: The systems phenotyping approach identified three clinically interpretable phenotypic partitions (MP, IHD, SIRD) along a metabolic continuum in patients evaluated for thyroid dysfunction. TyG values increased monotonically across partitions (medians 8.24, 8.74, 8.87) and provided a pragmatic single-number summary of the principal metabolic axis. The IHD phenotype indicates that hepatic dysregulation can emerge as a distinct early stage rather than solely a late consequence of insulin resistance. Metabolic and thyroid axes were near-orthogonal, positioning thyroid autoimmunity as a parallel modulatory layer. These findings support TyG as an accessible descriptor for multidimensional endocrine–metabolic risk stratification.

Background: Diabetes distress is a key mental health dimension of type 2 diabetes (T2D) and a growing concern as populations age, yet its determinants in older adults remain insufficiently characterized. The independent contributions of demographic and metabolic factors to severe distress and their implications for multidisciplinary screening across the lifespan remain unclear. Methods: In this cross-sectional study, we assessed 453 adults with T2D using the 17-item Diabetes Distress Scale (DDS-17). We categorized participants as experiencing low (<2), moderate (2–2.9), or severe (≥3) distress. We compared categories using the Kruskal–Wallis H test and chi-square tests. Logistic regression models were constructed with severe distress (DDS ≥ 3) as the outcome, adjusting for sex, educational level, and HbA1c. Results: Severe distress was identified in 19 participants (4.2%). Age differed significantly across DDS categories (Kruskal–Wallis H test, p = 0.003), with the severe group being significantly older (72.95 ± 4.38 years). Emotional burden was the dominant subscale across all groups. In multivariable logistic regression, increasing age (aOR = 1.163 per year; 95% CI: 1.055–1.282; p = 0.002) and lower educational level (high school vs. basic: aOR = 0.187; p = 0.020) were independently associated with severe distress, whereas metabolic variables were not independently associated with severe distress. ROC analysis identified age ≥ 70 years as an exploratory, sample-derived cut-off (AUC = 0.726, 95% CI 0.602–0.827; sensitivity 84%, specificity 55%). Results were robust in sensitivity analyses that excluded patients with diabetes duration < 5 years. Conclusions: Severe diabetes distress is more strongly associated with demographic vulnerability than with metabolic burden. Age ≥ 70 years and lower educational attainment may help identify older adults at increased risk of diabetes-related distress, supporting targeted psychological screening strategies in this lifespan stage.

Background: The growing use of digital health technologies in diabetes care offers new opportunities for self-management and clinical monitoring. However, there remains significant variability in the extent to which individuals engage with these digital tools. Understanding the psychosocial and clinical factors associated with the use of digital health technologies is crucial for developing targeted implementation strategies. Objectives: The aim of this study was to assess the use of digital health technologies among adults with diabetes and to explore their relationship with psychosocial factors—especially technology acceptance and self-efficacy—as well as certain clinical characteristics, including diabetes-related stress, age, and disease duration. Methods: We conducted a cross-sectional study involving 304 adults with diabetes. Digital engagement was measured using the Digital Adherence and Use Questionnaire (DAUQ), a 7-item self-report instrument (Cronbach’s α = 0.89), from which a composite Digital Engagement Score was calculated (range 1–5) to indicate the level of technology-related self-management behaviors. Participants were descriptively categorized into low- and high-engagement groups. Engagement patterns were also analyzed by diabetes type to understand structural differences in technology exposure. Relationships between psychosocial variables and the outcome were examined using correlation analyses. Since engagement among participants with type 1 diabetes (T1D) showed limited variability, multivariable regression analyses were performed on participants with type 2 diabetes (T2D) using beta regression, with linear regression as a sensitivity analysis. An exploratory beta regression was also conducted for T1D. Results: Overall, 35.5% of participants were classified as having high digital engagement. High engagement was observed in more than 90% of participants with T1D, compared to 4.1% of those with T2D. Median engagement scores differed significantly between low- and high-engagement groups (median [Q1–Q3]: 1.71 [1.71–2.39] vs. 3.86 [3.86–4.43]). Highly engaged participants reported much higher levels of openness to technology (median [Q1–Q3]: 5.00 [1.00–5.00] vs. 1.00 [1.00–1.00], p < 0.001) and self-efficacy (median [Q1–Q3]: 3.00 [3.00–3.00] vs. 5.00 [5.00–5.00], p < 0.001). In T1D, multivariable beta regression analyses showed that age was independently associated with digital engagement, with each 10-year increase corresponding to a decrease in engagement (β = −0.147, 95% CI −0.219 to −0.075, p < 0.001). Diabetes duration and psychosocial variables were not independently associated with engagement in the multivariable model. In contrast, among participants with T2D, insulin treatment emerged as the strongest independent predictor of engagement (β = 0.996, 95% CI 0.859–1.134, p < 0.001), and diabetes-related stress emerged as an independent predictor of engagement (β = 0.069, 95% CI 0.006–0.132, p = 0.033). Technology acceptance was positively associated with engagement (β = 0.694, 95% CI 0.350–1.037, p < 0.001), whereas higher self-efficacy was independently associated with lower engagement intensity (β = −0.366, 95% CI −0.608 to −0.124, p = 0.003). Age and diabetes duration were not independently associated with engagement after adjustment. Conclusions: Digital engagement appears to function as a structurally embedded component of self-management in T1D, with limited variability and largely independent of psychosocial modulation. In T2D, engagement is predominantly driven by treatment characteristics (insulin treatment), psychosocial dynamics (stress, technology acceptance), with higher self-efficacy associated with reduced reliance on digital tools. These findings suggest distinct behavioral mechanisms underlying digital health utilization across diabetes types and support the need for tailored implementation strategies.