UNASSIGNED: Although numerous studies have indicated the utility of waist-to-height ratio (WHtR) in early screening for individuals with adverse cardiometabolic health, there is controversy on using WHtR as a one-size-fits-all approach, including in older adults.
UNASSIGNED: Our study aims to identify the pooled diagnostic accuracy of WHtR in screening for metabolic syndrome (MetS) and its components among older adults.
UNASSIGNED: A systematic review of observational studies was performed using 4 databases. A diagnostic meta-analysis with a random effects model was conducted, and the pooled area under the summary receiver operating characteristic curve, sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (dOR) of each outcome compared with WHtR, body mass index (BMI), and waist circumference (WC) were calculated, with sex-stratified analysis.
UNASSIGNED: A total of 17 studies with 74,520 participants were included. As reflected by the dOR, WHtR (7.65; 95% CI: 6.00, 9.75) performed better than BMI (5.17; 95% CI: 4.75, 5.62) and WC (5.77; 95% CI: 4.60, 7.25) in screening for MetS among older adults and was potentially better among males. For hyperglycemia, hypertension, and dyslipidemia, the performances of WHtR, BMI, and WC were comparable.
UNASSIGNED: More studies focusing on older adults are still needed to determine the cutoff values of WHtR to screen for MetS.The search strategy was registered in PROSPERO as CRD42022350379.

Biomarkers are vital to detect diseases in various clinical stages. A variety of cancer serum biomarkers are already known, while for more accurate cancer-type detection, there required more rigorous evaluation manners, especially computational evaluation measures, for biomarkers. In this review, we first show three typical pitfalls in finding biomarkers and their examples, after briefly presenting standard five clinical biomarker screening phases by National Cancer Institute. We then introduce current computational biomarker evaluation measures, including current, standard methods with their intrinsic features. We further show an up-to-date list of existing cancer serum biomarkers, pointing out several issues, being caused by the limitations of current biomarker evaluation approaches. Finally we discuss the current attempts to develop new, statistically robust, computational serum-based biomarker measures in terms of specificity to each of various cancer types.

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker-based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55-0.68), which did not differ appreciably by study design, sample size, participants\' race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor-based models (median AUC, 0.79 (range, 0.63-0.91) vs. median AUC, 0.78 (range, 0.63-0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants\' race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance.