Objective: To investigate the characteristics of body fat distribution and the relationship between body fat index and Atherogenic Index of Plasma (AIP) in Type 2 Diabetes Mellitus (T2DM) patients. Methods: A total of 316 participants were divided into a T2DM group and a non-diabetes group (controls). According to the Visceral Fat Area (VFA), all participants were further divided into VFA ≥100 cm2 and VFA <100 cm2 groups. To compare the differences of blood lipid, blood glucose, body fat index and AIP between the 2 groups, single factor correlation analysis was used to determine the correlation between the indexes and AIP, and multiple linear regression was used to analyse the correlation between the related factors and AIP. Results: The body fat index (including body fat content, Percentage of Body Fat (PBF), Waist to Hip Fat Ratio (WHR) and VFA), Triglyceride (TG), Fasting Insulin (FINS), Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and AIP in T2DM group were significantly higher than in the control group, while High Density Lipoprotein Cholesterol (HDL-C) level was significantly higher in the control group. In the VFA ≥ 100 cm2 group, TG, Low Density Lipoprotein Cholesterol (LDL-C), FINS, HOMAIR and AIP were all higher than that in the VFA <100 cm2 group. There was a positive correlation between AIP and VFA, body fat content, percentage of body fat, and WHR, respectively. There was also a negative correlation between AIP and HDL-C, which was not related to age, sex, Fasting Glucose (FPG), glycosylated haemoglobin (HbA1c), Total Cholesterol (TC), LDL-C and course of disease. Compared with the VFA <100 cm2 group, the VFA ≥100 cm2 group had higher blood Uric Acid (UA) levels and UA was positively correlated with VFA. After correcting the effect of UA on AIP, VFA was still an independent related factor of AIP, and VFA increased the risk of atherosclerosis by increased UA. Conclusion: T2DM patients have the abnormal distribution of body fat and a high VFA, which was associated with AIP.

In this diagnostic test validation study, we assessed the clinical accuracy and HPV genotyping performance of the INNO-LiPA HPV Genotyping Extra II (INNO-LiPA) within the VALGENT-3 framework. VALGENT is designed to assess the analytical and clinical performance of HPV tests with genotyping capacity. The VALGENT-3 panel comprised 1300 consecutive cervical cell specimens enriched with 300 samples with abnormal cytology obtained from women attending the Slovenian cervical cancer screening programme. The INNO-LiPA allows type-specific detection of 32 HPV types; however, for the clinical accuracy assessment, we considered it as high-risk (hr)HPV positive when at least one of the following HPV types was present: HPV16, HPV18, HPV31, HPV33, HPV35, HPV39, HPV45, HPV51, HPV52, HPV56, HPV58, HPV59, and HPV68. Clinical accuracy for detection of cervical intraepithelial neoplasia grade 2 or worse (CIN2+) was compared between INNO-LiPA and Hybrid Capture 2 (HC2), which is a standard comparator test for HPV tests used in cervical cancer screening. In addition, hrHPV and type-specific detection HPV types were compared between INNO-LiPA and Linear Array HPV Genotyping Test (Linear Array). The prevalence of hrHPV determined by INNO-LiPA was 17.1% (95% CI, 15.0–19.2%) in the screening population. HrHPV testing with INNO-LiPA had a sensitivity for CIN2+ of 96.9% (95% CI, 92.1–99.1%) which was non-inferior to HC2 (relative sensitivity of 1.01; 95% CI, 0.97–1.04; p_n.inf = 0.0002) and a specificity for ≤CIN1 of 85.3% (95% CI, 83.2–87.3%) which was inferior to HC2 (relative specificity of 0.95; 95% CI, 0.93–0.97; p_n.inf = 0.9998). Genotyping agreement between INNO-LiPA and Linear Array was excellent for hrHPV, HPV16, HPV18, HPV35, HPV45, HPV58 and HPV59, but good or fair for other HPV types. To conclude, INNO-LiPA demonstrated non-inferior clinical sensitivity but lower specificity compared to HC2 in addition to excellent concordance compared to Linear Array for hrHPV and some genotypes.

OBJECTIVE: To estimate the incidence and identify risk factors for diagnosed type 2 diabetes (T2D) among young U.S. adults. RESEARCH DESIGN AND METHODS: We analyzed 142,884 adults aged 18–79 years with self-reported diabetes type from the cross-sectional National Health Interview Survey in 2016–2022, representing the noninstitutionalized U.S. civilian population. Incidence of diagnosed T2D was calculated for three age groups: young-adult onset (18–44 years), middle-age onset (45–64 years), and older-adult onset (65–79 years); the latter two groups were included to highlight the distinct risk factor profile of young-adult-onset T2D. Multivariable logistic regressions were used to identify risk factors for young-adult-onset T2D. RESULTS: The estimated incidence of diagnosed young-adult-onset T2D was 3.0 per 1,000 adults (95% CI 2.6–3.5). Minority groups, socioeconomically disadvantaged individuals, and people with cardiometabolic diseases or psychological conditions had a higher incidence of diagnosed young-adult-onset T2D compared with their counterparts. Lipid-lowering medication use (adjusted odds ratio [aOR] 13.15, 95% CI 8.85–19.55), antihypertensive medication use (aOR 11.89, 95% CI 7.97–17.73), and obesity (BMI ≥30 vs. <25 kg/m2, aOR 10.89, 95% CI 6.69–17.7) were the strongest risk factors for young-adult-onset T2D; these risk factors, along with hypertension, hyperlipidemia, and coronary heart disease, were more strongly associated with young-adult-onset T2D compared with later-onset T2D, with up to 4.5 times higher aORs. CONCLUSIONS: This study quantified the incidence of diagnosed young-adult-onset T2D in U.S. adults and identified its distinct risk factor profile. Targeted prevention strategies for young-adult-onset T2D are needed for minority and socioeconomically disadvantaged people and those with cardiometabolic diseases.

To describe the epidemiological, clinical, and household transmission characteristics of pediatric COVID-19 cases in Shanghai, China. Pediatric patients with COVID-19 hospitalized in Shanghai from March-May 2022 were enrolled in this retrospective, multicenter cohort study. The symptoms and the risk factors associated with disease severity were analyzed. In total, 2620 cases (age range, 24 days-17 years) were enrolled in this study. Of these, 1011 (38.6%) were asymptomatic, whereas 1415 (54.0%), 190 (7.3%), and 4 (0.2%) patients developed mild, moderate, and severe illnesses, respectively. Household infection rate was negatively correlated with household vaccination coverage. Children aged 0-3 years, those who are unvaccinated, those with underlying diseases, and overweight/obese children had a higher risk of developing moderate to severe disease than children aged 12-17 years, those who were vaccinated, those without any underlying disease, and those with normal weight, respectively (all P <0.05). A prolonged duration of viral shedding was associated with disease severity, presence of underlying diseases, vaccination status, and younger age (all P <0.05). Children aged younger than 3 years who were not eligible for vaccination had a high risk of developing moderate to severe COVID-19 with a prolonged duration of viral shedding. Vaccination could protect children from COVID-19 at the household level.

Only clinically validated human papillomavirus (HPV) tests should be used in cervical cancer screening. VALGENT provides a framework to validate new HPV tests. In the VALGENT-3 study, the clinical accuracy of the recently launched Abbott Alinity m HR HPV assay (Alinity m) to detect cervical precancerous lesions was assessed against the standard comparator test (Hybrid Capture 2; HC2) and additionally against two previously validated alternative comparator tests (Abbott RealTime HR HPV and Roche cobas 4800 assays). Validation was conducted using 1,300 consecutive cervical samples from women attending an organized population-based cervical screening program enriched with 300 cytologically abnormal samples. Overall hrHPV test concordance was assessed by kappa values; the concordance for HPV-16 and HPV-18 was assessed for Alinity m, RealTime, and cobas, and the Linear Array (Roche) was used for more detailed genotyping concordance. In the total study population, the relative sensitivity and specificity for CIN2+ and CIN3+ of Alinity m compared to HC2 was 1.02 (95% CI:0.99–1.06) and 1.03 (95% CI:0.99–1.06), respectively. The relative specificity for ≤ CIN1 was 1.01 (95% CI:1.00–1.02) (all p_ni ≤ 0.001). Alinity m showed non-inferior clinical accuracy among women 30 years or older when cobas or RealTime were used as comparators. HPV genotype-specific concordance between Alinity m and the three comparator tests showed excellent agreement, with kappa values ranging between 0.82 and 1.00. In conclusion, Alinity m fulfils the international accuracy requirements for use in cervical cancer screening and shows excellent HPV genotype-specific concordance with three clinically validated HPV tests.

The VALGENT framework is developed to assess the clinical performance of HPV tests that offer genotyping capability. Samples from the VALGENT-3 panel are used to identify an optimal viral concentration threshold for the RIATOL qPCR HPV genotyping assay (RIATOL qPCR) to assure non-inferior accuracy to detect high-grade cervical intraepithelial neoplasia (CIN), compared to Qiagen Hybrid Capture 2 (HC2), a standard comparator test validated for cervical cancer screening. The VALGENT-3 panel comprised 1300 samples from women participating in the Slovenian cervical cancer screening programme, enriched with 300 samples from women with abnormal cytology. In follow- up, 126 women were diagnosed with CIN2+ (defined as diseased) and 1167 women had two consecutive negative Pap smears (defined as non-diseased). All 1600 samples were analyzed with the RIATOL qPCR. Viral concentration was expressed as viral log10 of the number of copies/ml. A zone of viral concentration cut-offs was defined by relative ROC analysis where the sensitivity and specificity were not inferior to HC2. The RIATOL qPCR had a sensitivity and specificity for CIN2+ of 97.6% (CI: 93.2-99.5%) and 85.1% (CI: 82.9-87.1%), respectively, when the analytical cut off was used. At a cut off of 6.5, RIATOL qPCR had a sensitivity of 96.0% (CI: 91.0-98.7%) and a specificity of 89.5% (87.6-91.2%). At optimized cut off, accuracy of the qPCR was non-inferior to the HC2 with a relative sensitivity of 1.00 [CI: 0.95-1.05 (p = 0.006)] and relative specificity of 1.00 [CI: 0.98-1.01 (p = 0.0069)]. The RIATOL qPCR has a high sensitivity and specificity for the detection of CIN2 + . By using a fixed cut-off based on viral concentration, the test is non-inferior to HC2. HPV tests that provide viral concentration measurements or other quantifiable signals allow flexibility to optimize accuracy required for cervical cancer screening.