To investigate whether systemic inflammation, measured by the neutrophil-to-lymphocyte ratio (NLR), modifies the cross‑sectional association between insulin resistance (IR, assessed by HOMA-IR) and overall cancer history in a nationally representative population. We conducted a cross-sectional analysis utilizing data from the 2017–2018 National Health and Nutrition Examination Survey (NHANES), which employs a complex, multistage probability sampling design. All analyses accounted for the complex survey design and sampling weights to ensure national representativeness. Weighted multivariable logistic regression was used to assess the cross‑sectional association, with an interaction term between HOMA-IR and NLR. Among 3,746 adults, after adjusting for age, sex, and BMI, HOMA-IR was not independently associated with cancer history (OR = 1.00, 95
Autonomic dysfunction is an important but frequently under-recognized complication of type 2 diabetes mellitus (T2DM), particularly in settings where objective autonomic testing is not routinely available. The Composite Autonomic Symptom Score-31 (COMPASS-31) provides a practical symptom-based approach for assessing autonomic dysfunction across multiple domains. This study compared socioeconomic, lifestyle, and clinical characteristics among adults with T2DM in Zanzibar according to COMPASS-31–defined autonomic dysfunction and examined factors associated with its presence. This hospital-based cross-sectional study included 665 adults with T2DM attending the diabetes clinic at Mnazi Mmoja Hospital, Zanzibar. Autonomic symptoms were assessed using COMPASS-31, including the orthostatic intolerance, gastrointestinal, and bladder domains. Participants were classified as having autonomic dysfunction when they met the prespecified threshold for dysfunction in at least one assessed domain. Cardiovascular autonomic neuropathy (CAN) was assessed separately using standardized cardiovascular autonomic reflex tests performed with an automated CAN Analyzer and was not included in the COMPASS-31 composite outcome. Modified Poisson regression with robust variance estimation was used to calculate adjusted prevalence ratios (aPRs) and 95
The number of people seeking gender-affirming medical care and gender-affirming hormone therapy (GAHT) is increasing. As sex steroids shape cardiometabolic physiology, GAHT is expected to meaningfully alter cardiometabolic risk. The aim of this review was to systematically determine and narratively synthesize evidence on the cardiometabolic effects of GAHT and identify clinically relevant knowledge gaps. We searched PubMed for English-language primary human studies published through November 26, 2025. Of 346 screened records, 96 unique original investigations were included. Most studies had less than 3 years of follow-up, the longest being 20 years in one electronic health record-based study. In transgender women, BMI was often reported as stable with higher fat and lower lean mass compared to baseline; insulin resistance tended to increase while fasting glucose and HbA1c were usually longitudinally stable. Lipid changes often showed reductions in total and low-density lipoprotein cholesterol as well as triglyceride levels. Blood pressure generally remained stable or decreased. In transgender women, VTE incidence was higher than in both cisgender women and men, whereas stroke and MI incidence was higher primarily in comparison with cisgender women. In transgender men, BMI was increased or stable and lean mass increased with variable fat mass changes and some long-term increases in visceral and liver fat. Glucose usually remained stable while insulin resistance findings were mixed. Lipid profiles commonly shifted toward lower high-density lipoprotein cholesterol and higher low-density lipoprotein cholesterol and triglycerides. Blood pressure findings were mixed with some cohorts showing an increase. In transgender men, MI incidence was higher than in cisgender women but generally not higher than in cisgender men; evidence for other cardiovascular outcomes was limited and inconsistent. Overall, evidence on systemic inflammation, renal and hepatic outcomes is sparse. In conclusion, GAHT is associated with quantifiable cardiometabolic changes, but magnitude and clinical relevance vary by regimen, population, baseline risk and study follow-up duration. Heterogeneous regimens, study methods, and confounding contribute to mixed findings. Major gaps persist with limited long-term prospective data, sparse mechanistic insights, underrepresentation of adolescents and non-European ancestry populations, and insufficient information on cardiometabolic outcomes with ageing. Sex- and gender identity-stratified evidence synthesis: Distinguishes feminizing and masculinizing GAHT while presenting parallel outcome analyses for transgender women and transgender men, enabling more clinically precise interpretation of cardiometabolic effects. Systematic characterization of heterogeneity: Integrates regimen-, population-, and context-specific findings, including discordant results, into structured domain-specific evidence tables, improving transparency and interpretability across a heterogeneous literature base. Novel evidence mapping framework: Introduces a domain-level framework quantifying both evidence volume (number of primary studies) and directional concordance of findings, visualized within a sex-stratified cardiometabolic risk continuum to identify areas of consistency, uncertainty, and evidence gaps.
There are an estimated 9.5 million people living with type 1 diabetes (T1D) globally, with cardiovascular disease (CVD) remaining a major cause of morbidity and mortality despite advances in care. Atherosclerosis occurs earlier in people with T1D and those with T1D-onset in early childhood are at particularly high lifetime risk of CVD. Multiple traditional risk factors (e.g., glycaemia, lipids, blood pressure, obesity and smoking) and novel risk factors (e.g., social determinants of health, inflammation and genetics) can promote CVD in people with T1D. This highlights the need for a life-course approach to managing overall cardiovascular risk and preventing or mitigating end-stage complications such as atherosclerotic events, cardiac autonomic neuropathy and heart failure. However, most people with T1D do not meet all recommended risk factor targets. Whilst T1D-specific risk calculators for CVD are available to guide shared decision-making and risk factor management, greater awareness and further data are needed to inform their use in different T1D populations. However, there remains a relative paucity of T1D-specific clinical trial data to inform the management and therapies for CVD risk factors such as dyslipidaemia, hypertension and chronic kidney disease (CKD), and for clinically evident CVD. There is now increasing interest in the use of adjunct glucose-lowering drugs initially developed for use in type 2 diabetes, such as sodium-glucose co-transporter 2 inhibitors and glucagon-like peptide-1 agonists, and whilst some weight, glycaemia, blood pressure and albuminuria benefits have been shown, cardiovascular outcome trials (CVOT) in T1D are currently lacking. This review by a multidisciplinary team provides an overview of CVD pathophysiology, risk factor care, risk prediction and management in people with T1D.
Phenotypic age gap (PAG) and phenotypic age acceleration (PhenoAgeAccel) are measures of biological ageing that have been linked to adverse health outcomes. However, their relationships with common chronic diseases and mortality in nationally representative populations remain incompletely understood. We investigated the associations of PhenoAge-based ageing metrics with chronic disease burden and all-cause mortality in U.S. adults. This retrospective study included 7,624 adults from NHANES 2005–2010 and 2015–2018. Phenotypic age was calculated using chronological age and nine clinical biomarkers. PAG was defined as phenotypic age minus chronological age, and PhenoAgeAccel was derived as a binary ageing-status variable. Cross-sectional analyses examined associations between common chronic diseases and ageing metrics, while machine-learning models were used to identify key predictive factors. Longitudinal analyses evaluated associations with all-cause mortality. Obesity, hypertension, arthritis, diabetes, and malignancy were among the conditions most strongly associated with accelerated biological ageing. Among the evaluated machine-learning algorithms, LightGBM showed the best predictive performance, and SHAP analyses identified obesity, hypertension, arthritis, diabetes, and malignancy as the most influential predictive features. Positive PhenoAgeAccel was associated with increased all-cause mortality(HR 5.52, 95
