Wiley最新文献

Background: To evaluate and recalibrate commonly used 10-year cardiovascular disease (CVD) risk prediction models in two Chinese cohorts: the general population and adults with type 2 diabetes.

Methods: We analysed data from the Wuzhong subcohort of the China Kadoorie Biobank (CKB) and the Jiangsu Biobank for the Prevention and Control of Diabetes (JBPCD). After excluding participants with baseline CVD, baseline diabetes in CKB or missing predictors, 42 937 CKB participants and 14 125 JBPCD participants were included. We evaluated CKB-CVD, Globorisk, Framingham General CVD and WHO 2019 in both cohorts, and SCORE2-Diabetes in JBPCD. Performance of original and recalibrated models was assessed by 10-year inverse probability of censoring weighting (IPCW) C-statistic, calibration measures including the expected-to-observed (E/O) event ratio and Brier score. In JBPCD, overlap in high-risk classification was examined using a 20% predicted 10-year risk threshold.

Results: During a median follow-up of 11.96 years in the general population cohort and 10.03 years in the type 2 diabetes cohort, 3740 and 2794 ischemic heart disease or stroke events occurred, with 10-year risks of 6.04% and 19.72%, respectively. Discrimination was higher in the general population cohort than in the type 2 diabetes cohort (C-statistic 0.723-0.778 vs. 0.584-0.629). Original models were poorly calibrated, with systematic overprediction in the general population cohort (E/O 1.47-3.78) and heterogeneous miscalibration in the type 2 diabetes cohort (E/O 0.32-2.70). Recalibration markedly improved calibration and reduced Brier scores, with little change in discrimination. In the type 2 diabetes cohort, recalibrated CKB-CVD classified 48.4% as high risk, with an observed 10-year risk of 25.7%.

Conclusion: Widely used 10-year CVD risk models showed acceptable or modest discrimination but substantial miscalibration in Chinese populations, especially among adults with type 2 diabetes. Recalibration is essential before threshold-based clinical or public-health use.

Background and aims: Brief interventions delivered in healthcare settings represent a promising substance use prevention and early intervention strategy for youth in foster care, among whom early life adversity and resulting psychological sequalae increase risk for problematic substance use. Limited research has examined the effectiveness of brief interventions in this subpopulation. This study aimed to examine whether psychological diagnoses were associated with screening positive for problematic substance use, and whether psychological diagnoses impacted effectiveness of a brief intervention for substance use.

Design: Longitudinal observational study of adolescents (ages 10-20 years) seen for mandated health examinations in a foster care clinic. Youth who endorsed substance use and received a brief intervention were invited to complete baseline surveys and follow-ups at 30 days, 60 days and 6 months. Logistic regressions evaluated associations of mental health disorders with endorsing problematic substance use. Generalized linear mixed models examined associations of mental health disorders with trajectories of post-intervention change in substance use.

Setting: Midwest, United States.

Participants: Adolescents seen for mandated health examinations in a foster care clinic who completed a substance use screening measure [CRAFFT2.1 + N; n = 1656; mean age (SD) = 14.4 (2.6); 53.4% female; 58.4% Black, 31.6% white]. A second subsample included only those adolescents who received a brief intervention for substance use during their clinic visit [n = 287; mean age (SD) = 15.9 (2.1); 59.9% female; 53.7% Black, 35.5% white].

Measurements: Mental health diagnoses and CRAFFT2.1 + N were extracted from the electronic health record. Youth-reported count of past 30-day use of alcohol, cannabis and nicotine/tobacco at each wave.

Findings: A minority (12%) of youth in foster care endorsed problematic substance use. Youth with a mental health diagnosis had 58% to 161% higher odds of reporting problematic substance use [odds ratios (ORs) = 1.58-2.61; 95% confidence intervals (CIs) = (1.16-2.13) - (1.93-3.56)]. Following the intervention, youth who had been diagnosed with internalizing disorders (i.e. anxiety, depressive and trauma disorders), and those with co-occurring internalizing and externalizing disorders, reported smaller decreases or no decreases in their cannabis [ORs = 2.09, 7.03; 95% CIs = (1.17-3.71), (3.43-14.42)] and nicotine use [ORs = 1.95-7.68; 95% CIs = (1.08-3.53) - (4.12-14.32)]. Alcohol use did not vary as a function of mental health diagnoses [ORs = 0.31-13.10; 95% CIs = (0.00-19.37) - (0.19-915.80).

Conclusion: Foster youth in the United States, particularly those with mental health concerns, appear to be at high risk for problematic substance use.

Aims: To establish age- and sex-specific reference percentiles for body shape indices in Chinese adults and to develop a composite body shape index (CBSI) for age-related chronic diseases screening.

Materials and methods: From the China National Health Survey, we recruited adults across six provinces from 2023 to 2025. Sex- and age-specific percentile curves for body shape indicators were constructed using the Lambda-Mu-Sigma method. CBSI was derived by sex-stratified exploratory factor analysis with varimax rotation, integrating adiposity, muscle function, and body proportion. Associations with chronic diseases were assessed in a training population (n = 11 220) and validated in an independent population (n = 5745). Incremental value over BMI was evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI).

Results: Body shape index trajectories differed substantially by sex. Compared with CBSI < -0.5, CBSI > 0.5 was associated with elevated odds of hypertension (OR 6.42, 95% CI 5.51-7.48), diabetes (2.76, 2.25-3.38), dyslipidemia (3.86, 3.37-4.41), hyperuricemia (5.44, 4.67-6.34), abnormal ECG (1.63, 1.24-2.13), coronary heart disease (2.31, 1.62-3.27), stroke (3.05, 1.95-4.78), chronic kidney disease (3.06, 1.79-5.23), and musculoskeletal disorders (1.67, 1.09-2.55). These associations persisted in the validation set and individuals aged > 60 years. Adding CBSI to BMI-based models improved disease identification, with positive IDI and category-free NRI.

Conclusions: Age- and sex-specific reference centiles for body shape indicators can reflect trajectories of body composition and muscle health across the lifespan, which facilitates sarcopenia screening. The CBSI serves as an integrated screening tool for high-risk populations.

Aims: Diabetes prediction tools are valuable for prevention and resource planning. Few tools exist to predict long-term diabetes risk for British adults. We aim to develop and validate a diabetes prediction tool for short- and long-term risk.

Materials and methods: Prediction model development (Training, n = 4502) and internal validation (Test, n = 1929) used Whitehall II data (WHII); external validation used English Longitudinal Study of Ageing (ELSA) data, with incident diabetes as outcome. Predictors were selected using LASSO-Cox, random survival forests (RSF) and best subset selection. Three candidate Cox models were built from these predictor sets and compared. Performance was assessed with C-indices, calibration and decision curve analysis (DCA). A continuous risk score and quartile-based risk groups were derived; a nomogram was created.

Results: Across WHII Test (235 events) and ELSA (255 events), three models showed broadly similar discrimination, calibration and DCA patterns. LASSO- and RSF-selected models performed comparably and slightly better than best subset. LASSO and RSF both selected ethnicity, body mass index, waist-to-height ratio, height, triglyceride-glucose index, fasting glucose, family history of diabetes and socioeconomic status; LASSO additionally selected smoking, whereas RSF also included age, triglycerides, high-density lipoprotein cholesterol, systolic and diastolic blood pressure. In ELSA, risk-score C-indices (95% CIs) were 0.800 (0.773-0.827) for the LASSO-selected model and 0.801 (0.774-0.827) for the RSF-selected model; risk-group C-indices were 0.737 (0.715-0.759) and 0.727 (0.707-0.748), respectively.

Conclusions: We developed and externally validated a tool to estimate 5-, 10- and 15-year diabetes risk in British middle-aged adults, which may support primary care decision-making.

Background: Diabetic kidney disease (DKD), a major microvascular complication of diabetes, is the leading cause of chronic kidney disease (CKD) and progression to end-stage kidney disease (ESKD). DKD exhibits substantial clinical heterogeneity, ranging from the classic proteinuric phenotype to the increasingly recognised nonproteinuric phenotype. However, cardiovascular, kidney and all-cause mortality risks across these phenotypes remain incompletely characterised.

Methods: We conducted a retrospective cohort study of 14 367 adults with DKD in the China Renal Data System (CRDS). Major adverse cardiovascular events (MACEs), ESKD and all-cause mortality were ascertained from longitudinal electronic health records. Multi-state models were used to characterise transitions among clinical states and to estimate transition-specific risks across DKD phenotypes.

Results: Over a median follow-up of 2.1 years, 17.1% of participants experienced MACE, while 7.0% and 10.0% progressed to ESKD and experienced all-cause mortality, respectively. High urinary albumin-to-creatinine ratio (UACR)-related phenotypes emerged as persistent risk factors across various disease trajectories. In particular, the phenotype combining low estimated glomerular filtration rate (eGFR) and high UACR had the highest risks of progression to ESKD and of death after MACE.

Conclusions: Our findings show that DKD phenotypes defined by baseline eGFR and UACR were associated with distinct cardiovascular, kidney and mortality risks. Albuminuria remained an important prognostic marker even when eGFR was preserved, whereas the combination of low eGFR and high albuminuria identified the highest risk group. These findings support improved phenotypic risk stratification and may inform future clinical research and individualised risk assessment in DKD.

Aims: Evaluating the association between polyendocrine metabolic ovarian syndrome (PMOS), traditionally termed polycystic ovary syndrome, and the broad spectrum of gestational dysglycemia in a large, universally screened population.

Materials and methods: This retrospective cohort study used routinely collected data linked from Israeli military pre-recruitment medical evaluation (ages 16-19) and Maccabi Healthcare Services electronic health records, including two-step GDM screening during the first pregnancy, 2001-2019. Outcome was categorised into gestational normoglycemia, abnormal glucose challenge test (GCT) with a normal oral glucose tolerance test (OGTT), gestational impaired glucose tolerance (GIGT) and gestational diabetes mellitus (GDM). Multivariable logistic models were adjusted for birth year, adolescent BMI, socioeconomic position, country of birth, cognitive performance, education and age at GDM screening.

Results: Among 177 241 women, 13 648 (7.7%) had PMOS. Rates (95% CI) of abnormal GCT with normal OGTT, GIGT, GDM and any dysglycemia were 9.1% (8.6-9.6) versus 7.9% (7.8-8.0), 4.9% (4.5-5.3) versus 4.0% (3.9-4.1), 6.3% (5.9-6.7) versus 4.2% (4.1-4.3) and 20.3% (19.6-21.0) versus 16.1% (16.0-16.3) among women with versus without PMOS, respectively. Corresponding adjusted ORs (95% CI) were 1.18 (1.11-1.26), 1.26 (1.16-1.37), 1.61 (1.49-1.73) and 1.31 (1.25-1.37), respectively. An interaction was observed with the highest odds among women with both PMOS and high adolescent BMI (adjusted OR 1.77, 95% CI: 1.52-2.10).

Conclusions: Women with PMOS, and particularly those with concomitantly high BMI, constitute a high-risk subgroup for subthreshold gestational dysglycemia and GDM, highlighting the importance of pre-pregnancy mitigation of modifiable risk factors.

Aim: Lipodystrophy syndromes are rare diseases with metabolic and cardiovascular consequences. This study explores the metabolic and renal effects of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in patients with genetic lipodystrophy.

Patients and methods: Patients with familial partial lipodystrophy (n = 57) or congenital generalised lipodystrophy (n = 2) (86% women) were initiated with SGLT2i in the French reference network PRISIS. We retrospectively collected clinical and biological data before initiation, after 6-18 months (short-term, mean ± SD 12.5 ± 6.4 months) and at the latest follow-up on SGLT2i (long-term, 29.0 ± 10.7 months).

Results: Body mass index decreased from 25 [22.9-28] (median [IQR]) to 24.4 [22.1-26.5] then 23.9 [22.1-26.4] kg/m² (p < 0.001) and HbA1c from 7.8 [7.1-8.8] to 7.3 [6.7-7.7] and 7.3% [6.9-8] (p < 0.01 and p = 0.05, respectively). Systolic blood pressure (130 [120-143.25] to 126 [120-137] mmHg), ALT (33 [23.5-44] to 30 [26-40] IU/L), GGT (44 [28.5-62.5] to 37 [27.85-45.5] IU/L) and liver stiffness (6.8 [5.9-11.8] to 5.8 [5-6.9] kPa) declined significantly at short-term (p < 0.05, 0.02, 0.02 and < 0.01, respectively). The dual-energy X-ray absorptiometry-based fat mass percentage (23.6 [18-29.3] to 20.8 [17.8-25.3], p = 0.07) and truncal-to-lower limb fat ratio (1.36 [1.18-1.50] to 1.17 [0.98-1.41], p = 0.08) tended to decrease at short-term. Urinary albumin-to-creatinine ratio, when elevated at baseline (> 3 mg/mmol, n = 18 patients), decreased from 10.8 [6.3-24.9] to 8.0 mg/mmol [2.8-14.4] (p = 0.02). Treatment discontinuation (7%) was mainly due to vaginal mycoses. No serious adverse events were reported.

Conclusion: In a real-life setting, SGLT2i therapy is associated with improvement in HbA1c, body weight, blood pressure, hepatic parameters and albuminuria in patients with genetic lipodystrophy.

Aims: This study aimed to perform an exploratory characterisation of metabolic phenotypes in primary care patients with type 2 diabetes mellitus (T2DM), using a predefined approach and examine their association with vitamin D status.

Materials and methods: This cross-sectional study included 178 participants randomly selected in Sergipe, Brazil. Phenotypic clusters were derived using body mass index (BMI), glycated haemoglobin levels, age at diagnosis, insulin resistance (homeostatic model assessment (HOMA) for insulin resistance) and β-cell function (HOMA-B). To explore differences across phenotypes, we evaluated high-sensitivity C-reactive protein [hs-CRP] and serum ferritin levels, waist circumference and BMI, serum concentrations of 25-hydroxyvitamin D [25(OH)D] and therapeutic profile. Clustering was performed using the K-medoids algorithm. Group comparisons were performed using the Kruskal-Wallis and Fisher's exact tests and associations were assessed using multivariable logistic regression. A p-value of < 0.05 was considered significant.

Results: Four metabolic phenotypes were identified: two more severe clusters (severe insulin-deficient diabetes (SIDD)-like and severe insulin-resistant diabetes-like) and two milder clusters (mild obesity-related diabetes (MOD)-like and mild age-related diabetes (MARD)-like). Significant differences were observed in age, insulin use, waist circumference and fasting glucose, triglycerides and hs-CRP levels. Median 25(OH)D levels were lower in severe phenotypes compared with milder phenotypes. The SIDD-like group, characterised by marked insulin deficiency, showed 3.68-fold higher odds of vitamin D insufficiency (< 30 ng/mL) compared with the MARD-like phenotype (p = 0.013).

Conclusions: The metabolic phenotypes were explored in a primary care T2DM cohort, supporting their reproducibility across clinical settings. Vitamin D status varied by phenotype severity, with more severe phenotypes more likely to exhibit insufficiency.