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AIMS: To assess whether a whole-herd lameness score on a New Zealand dairy farm in spring could predict lameness prevalence on the same farm in summer (and vice versa) and whether a single-herd lameness score could be used to determine whether herd lameness prevalence was < 5% in both spring and summer. METHODS: Prevalence data (proportion of the herd with lameness score ≥ 2 and with score 3; 0-3 scale) from a study where 120 dairy farms across New Zealand were scored in spring and in the following summer were analysed using limits-of-agreement analysis. In addition, farms were categorised as having either acceptable welfare (lameness prevalence < 5% in both spring and summer) or not (lameness prevalence ≥ 5% in either spring or summer or both). The accuracy and specificity of a single, whole-herd lameness score at identifying herds with acceptable welfare were then calculated. RESULTS: The limits-of-agreement analysis suggests that 95% of the time, the prevalence of lameness in summer would be expected to be between 0.23 and 4.3 times that of the prevalence in spring. The specificity and accuracy of identifying a farm as acceptable on both occasions from a single observation were, respectively, 74% and 92% in spring, and 59% and 87% in summer. CONCLUSIONS: A single, one-off, whole-herd lameness score does not accurately predict future lameness prevalence. Similarly, acceptable status (lameness prevalence < 5%) in one season is not sufficiently specific to be used to predict welfare status in subsequent seasons. CLINICAL RELEVANCE: Whole-herd lameness scoring should be used principally as a means of detecting lame cows for treatment. A single whole-herd lameness score by an independent assessor should not be used to determine a herd's welfare status.

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PURPOSE: We aimed to investigate the predictive potential of plasma connecting peptide (C-peptide) in differentiating type 1 diabetes (T1D) from type 2 diabetes (T2D) and to inform evidence-based diabetes classification criteria. METHODS: A retrospective review was performed of all the patients with diabetes visiting an outpatient diabetology, endocrinology, general practice and family medicine tertiary health care center between January 2016 and December 2021. FINDINGS: Two hundred twelve individuals with diabetes were included, 85 (44.8%) with T1D and 127 (55.2%) with T2D. Mean (SD) age at diagnosis was 35.9 (15.1) years, and 112 (52.8%) men. Median (interquartile range [IQR]) duration of diabetes was 3.8 (3.0-4.5) years (T1D, 3.9 [3.5-4.6]; T2D, 3.4 [2.4-4.4]; P = 0.001). Body mass index was <18.5 kg/m2 in 5 (2.5%) individuals (T1D, 5; T2D, none), 18.5 to <25 kg/m2 in 57 (28.5%) (T1D, 32; T2D, 25), 25 to <30 kg/m2 in 58 (29%) (T1D, 28; T2D, 30), and >30 kg/m2 in 80 (40.0%) (T1D, 20; T2D, 60). Median (IQR) glycosylated hemoglobin was 7.4% (6.7%-8.5%) (T1D, 8.3% [7.2%-9.9%]; T2D, 7% [6.3%-7.6%]; P = 0.0001). Median (IQR) C-peptide concentration was 0.59 nmol/L (0.01-1.14 nmol/L) (T1D, 0.01 nmol/L [0.003-0.05 nmol/L]; T2D, 1.03 nmol/L [0.70-1.44 nmol/L]; P = 0.0001). C-peptide concentration of ≤0.16 nmol/L showed 92.9% sensitivity, 1-specificity of 2.4%, and AUC of 97.2% (CI, 94.7%-99.6%; P = 0.0001) in differentiating T1D from T2D. IMPLICATIONS: To our knowledge, this is the first study in the Middle East and North Africa region highlighting the role of C-peptide in diabetes classification. The estimated cutoff point for C-peptide concentration (≤0.16 nmol/L) will certainly help in accurately classifying the T1D and will rule out the routine clinical judgmental approaches in the region, especially in those scenarios and periods where it is always difficult to diagnose the diabetes type. Quantifying the cutoff for C-peptide is among the vital strengths of this study that will provide a better treatment plan in diabetes care management. Also, we evaluated concomitant glucose levels to rule out the phenomenon of falsely low C-peptide values in the setting of hypoglycemia or severe glucose toxicity. Based on our findings, C-peptide testing could be included in postulating an evidence-based guideline that differentiates T1D from T2D. Despite this, our study has some limitations, including the selection bias due to the retrospective design and low C-peptide levels could be indicative of low pancreatic reserves due to other causes or long-standing T2D, and quantifying these reasons requires additional resources and time.

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Genomic prediction has mostly been used in single environment contexts, largely ignoring genotype x environment interaction, which greatly affects the performance of plants. However, in the last decade, prediction models including marker x environment (MxE) interaction have been developed. We evaluated the potential of genomic prediction in red clover (Trifolium pratense L.) using field trial data from five European locations, obtained in the Horizon 2020 EUCLEG project. Three models were compared: (1) single environment (SingleEnv), (2) across environment (AcrossEnv), (3) marker x environment interaction (MxE). Annual dry matter yield (DMY) gave the highest predictive ability (PA). Joint analyses of DMY from years 1 and 2 from each location varied from 0.87 in Britain and Switzerland in year 1, to 0.40 in Serbia in year 2. Overall, crude protein (CP) was predicted poorly. PAs for date of flowering (DOF), however ranged from 0.87 to 0.67 for Britain and Switzerland, respectively. Across the three traits, the MxE model performed best and the AcrossEnv worst, demonstrating that including marker x environment effects can improve genomic prediction in red clover. Leaving out accessions from specific regions or from specific breeders' material in the cross validation tended to reduce PA, but the magnitude of reduction depended on trait, region and breeders' material, indicating that population structure contributed to the high PAs observed for DMY and DOF. Testing the genomic estimated breeding values on new phenotypic data from Sweden showed that DMY training data from Britain gave high PAs in both years (0.43-0.76), while DMY training data from Switzerland gave high PAs only for year 1 (0.70-0.87). The genomic predictions we report here underline the potential benefits of incorporating MxE interaction in multi-environment trials and could have perspectives for identifying markers with effects that are stable across environments, and markers with environment-specific effects.

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Background: The coronary angiography-derived index of microvascular resistance (caIMR) correlates well with the index of microcirculatory resistance (IMR), which predicts microvascular obstruction (MVO). However, the relationship between caIMR and MVO remains unclear. Aim: To evaluate the predictive ability of caIMR of MVO after ST-segment elevation myocardial infarction (STEMI). Methods: CaIMR was calculated using computational flow and pressure simulation in patients with STEMI in whom MVO status had been assessed by cardiac magnetic resonance (CMR) after successful primary percutaneous intervention at Peking University First Hospital between December 2016 and August 2019. The clinical, biochemical, echocardiographic, and CMR characteristics were assessed according to MVO status. The predictive value of the clinical parameters and caIMR was evaluated. Results: Fifty-three eligible patients were divided into an MVO group (n = 32) and a no-MVO group (n = 21). The caIMR tended to be higher in the MVO group (41.6 U vs. 30.1 U; p = 0.136). CaIMR and peak cardiac troponin-I (cTNI) were independent predictors of MVO (per 1-U increment in caIMR: odds ratio [OR] 1.044, 95% confidence interval [CI] 1.004-1.086, p = 0.030; per 1 ng/L increase in peak cTNI: OR 1.018, 95% CI 1.003-1.033, p = 0.022). In receiver-operating characteristic curve analysis, when a cut-off value of 45.17 U was used, caIMR had some ability to predict MVO (area under the curve 0.622, 95% CI 0.478-0.752, p = 0.127). Conclusions: CaIMR and peak cTNI were independent predictors of short-term MVO in patients with STEMI who had undergone successful primary percutaneous coronary intervention and may help to identify those at high risk of MVO.

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OBJECTIVES: The study aimed to evaluate a simplified and practical frailty detection tool derived from the Fried frailty phenotype (FFP). This tool was developed to facilitate the identification of frail individuals in constrained settings, addressing the challenges posed by uncertain cutoffs of FFP indicators in prompt frailty assessment. DESIGN: A longitudinal study and a cross-sectional study. SETTINGS AND PARTICIPANTS: A total of 1978 older adults aged 67.4 ± 6.16 years from the China Health and Retirement Longitudinal Study (CHARLS), and 972 older adults aged 72.8 ± 6.75 years from a pilot cross-sectional study conducted in Shanghai communities. METHODS: Frailty was assessed according to the FFP criterion. A Chinese modified frailty phenotype (CMFP) was developed, incorporating specific cutoffs for grip strength and an alternative test for walk speed. The internal consistency reliability, the criterion, and predictive validity of the CMFP were evaluated. RESULTS: The 5-time chair stand test (5t-CST) was significantly associated with the 2.5-m walk test (r = 0.373 in the CHARLS and 0.423 in the pilot study). Each element of the CMFP showed moderate to strong correlations with the total CMFP score and showed Cronbach's alpha of 0.303 and 0.358 in both populations. The Spearman's r and kappa values between the CMFP and the FFP were 0.795 and 0.663 in the CHARLS, and 0.676 and 0.537 in the pilot study. The areas under the curve (AUC) were 0.936 and 0.928 in the 2 studies, respectively. In addition, frailty assessed by the CMFP significantly predicted future incidence of outcomes, including all-cause mortality, activities of daily living (ADL)/instrumental ADL disability, hospitalization, and depression. CONCLUSIONS AND IMPLICATIONS: The study demonstrated the CMFP as a valid tool, particularly highlighting its excellent predictive ability on outcomes. The 5t-CST may act as a viable alternative test for assessing slowness. The CMFP can be systematically integrated into preclinical practice to identify frail individuals, especially within constrained spaces.

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Background: Currently, there is a lack of well-established markers to predict the efficacy of chemoimmunotherapy in small-cell lung cancer (SCLC). Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), advanced lung cancer inflammation index (ALI) and prognostic nutritional index (PNI) are associated with prognosis in several tumors, whereas their predictive role in SCLC remains unclear. Methods: A retrospective study was conducted at Sun Yat-sen University Cancer Center, involving extensive-stage SCLC (ES-SCLC) patients who received first-line chemoimmunotherapy between January 2020 and December 2021. Peripheral blood biomarkers were extracted from medical records and their correlation with prognosis and immune-related adverse events (IRAEs) was analyzed. Results: A total of 114 patients were included. Patients with a low PLR, high ALI and high PNI had prolonged progression-free survival (PFS) compared to those with a high PLR, low ALI and low PNI. Patients with a low NLR, low PLR, high ALI and high PNI had prolonged overall survival (OS) compared to those with a high NLR, high PLR, low ALI and low PNI. Cox regression model showed that PNI was an independent risk factor for both PFS and OS. ROC curve showed that PNI outperforms NLR, PLR and ALI in predicting both PFS and OS. The PNI-based nomogram demonstrated strong predictive capability for both PFS and OS. In addition, there was a significant correlation between PNI and IRAEs. Conclusion: A high baseline PNI might be associated with improved prognosis and the occurrence of IRAEs in ES-SCLC patients treated with first-line chemoimmunotherapy.

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BACKGROUND: The accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program. RESULTS: Our results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction. CONCLUSIONS: The dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

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BACKGROUND: The predictive ability of the early determination of sex steroids and the total testosterone:estradiol ratio for the risk of severe coronavirus disease 2019 or the potential existence of a biological gradient in this relationship has not been evaluated. OBJECTIVES: To assess the relationship of sex steroid levels and the total testosterone:estradiol ratio with the risk of severe acute respiratory syndrome coronavirus 2 infection in men, defined as the need for intensive care unit admission or death, and the predictive ability of each biomarker. MATERIALS AND METHODS: This was a prospective observational study. We included all consecutive adult men with severe acute respiratory syndrome coronavirus 2 infections in a single center admitted to a general hospital ward or to the intensive care unit. Sex steroids were evaluated at the centralized laboratory of our hospital. RESULTS: We recruited 98 patients, 54 (55.1%) of whom developed severe coronavirus disease in 2019. Compared to patients with nonsevere coronavirus disease 2019, patients with severe coronavirus disease 2019 had significantly lower serum levels of total testosterone (111 ± 89 vs. 191 ± 143 ng/dL; p < 0.001), dehydroepiandrosterone (1.69 ± 1.26 vs. 2.96 ± 2.64 ng/mL; p < 0.001), and dehydroepiandrosterone sulfate (91.72 ± 76.20 vs. 134.28 ± 98.261 µg/dL; p = 0.009), significantly higher levels of estradiol (64.61 ± 59.35 vs. 33.78 ± 13.78 pg/mL; p = 0.001), and significantly lower total testosterone:estradiol ratio (0.28 ± 0.31 vs. 0.70 ± 0.75; p < 0.001). The lower the serum level of androgen and the lower the total testosterone:estradiol ratio values, the higher the likelihood of developing severe coronavirus disease 2019, with the linear trend in the adjusted analyses being statistically significant for all parameters except for androstenedione (p = 0.064). In the receiver operating characteristic analysis, better predictive performance was shown by the total testosterone:estradiol ratio, with an area under the curve of 0.77 (95% confidence interval 0.68-0.87; p < 0.001). DISCUSSION AND CONCLUSION: Our results suggest that men with severe acute respiratory syndrome coronavirus 2 infection, decreased androgen levels and increased estradiol levels have a higher likelihood of developing an unfavorable outcome. The total testosterone:estradiol ratio showed the best predictive ability.

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This study aimed to assess the predictive ability of parametric models and artificial neural network method for genomic prediction of the following indicator traits of resistance to gastrointestinal nematodes in Santa Inês sheep: packed cell volume (PCV), fecal egg count (FEC), and Famacha© method (FAM). After quality control, the number of genotyped animals was 551 (PCV), 548 (FEC), and 565 (FAM), and 41,676 SNP. The average prediction accuracy (ACC) calculated by Pearson correlation between observed and predicted values and mean squared errors (MSE) were obtained using genomic best unbiased linear predictor (GBLUP), BayesA, BayesB, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayesian regularized artificial neural network (three and four hidden neurons, BRANN_3 and BRANN_4, respectively) in a 5-fold cross-validation technique. The average ACC varied from moderate to high according to the trait and models, ranging between 0.418 and 0.546 (PCV), between 0.646 and 0.793 (FEC), and between 0.414 and 0.519 (FAM). Parametric models presented nearly the same ACC and MSE for the studied traits and provided better accuracies than BRANN. The GBLUP, BayesA, BayesB and BLASSO models provided better accuracies than the BRANN_3 method, increasing by around 23% for PCV, and 18.5% for FEC. In conclusion, parametric models are suitable for genome-enabled prediction of indicator traits of resistance to gastrointestinal nematodes in sheep. Due to the small differences in accuracy found between them, the use of the GBLUP model is recommended due to its lower computational costs.

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People's networks are considered key in explaining fertility outcomes-whether people want and have children. Existing research on social influences on fertility is limited because data often come from small networks or highly selective samples, only few network variables are considered, and the strength of network effects is not properly assessed. We use data from a representative sample of Dutch women reporting on over 18 000 relationships. A data-driven approach including many network characteristics accounted for 0 to 40% of the out-of-sample variation in different outcomes related to fertility preferences. Individual characteristics were more important for all outcomes than network variables. Network composition was also important, particularly those people in the network desiring children or those choosing to be childfree. Structural network characteristics, which feature prominently in social influence theories and are based on the relations between people in the networks, hardly mattered. We discuss to what extent our results provide support for different mechanisms of social influence, and the advantages and disadvantages of our data-driven approach in comparison to traditional approaches.

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Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01423-y.

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Genomic estimated breeding values (GEBV) of animals without phenotypes can be indirectly predicted using recursions on GEBV of a subset. To maximize predictive ability of indirect predictions (IP), the subset must represent the independent chromosome segments segregating in the population. We aimed to 1) determine the number of animals needed in recursions to maximize predictive ability, 2) evaluate equivalency IP-GEBV, and 3) investigate trends in predictive ability of IP derived from recent vs. distant generations or accumulating phenotypes from recent to past generations. Data comprised pedigree of 825K birds hatched over 12 overlapping generations, phenotypes for body weight (BW; 820K), residual feed intake (RF; 200K) and weight gain during a trial period (WG; 200K), and breast meat percent (BP; 43K). A total of 154K birds (last six generations) had genotypes. The number of animals that maximize predictive ability was assessed based on the number of largest eigenvalues explaining 99% of variation in the genomic relationship matrix (1Me = 7,131), twice (2Me), or a fraction of this number (i.e., 0.75, 0.50, or 0.25Me). Equivalency between IP and GEBV was measured by correlating these two sets of predictions. GEBV were obtained as if generation 12 (validation animals) was part of the evaluation. IP were derived from GEBV of animals from generations 8 to 11 or generations 11, 10, 9, or 8. IP predictive ability was defined as the correlation between IP and adjusted phenotypes. The IP predictive ability increased from 0.25Me to 1Me (11%, on average); the change from 1Me to 2Me was negligible (0.6%). The correlation IP-GEBV was the same when IP were derived from a subset of 1Me animals chosen randomly across generations (8 to 11) or from generation 11 (0.98 for BW, 0.99 for RF, WG, and BP). A marginal decline in the correlation was observed when IP were based on GEBV of animals from generation 8 (0.95 for BW, 0.98 for RF, WG, and BP). Predictive ability had a similar trend; from generation 11 to 8, it changed from 0.32 to 0.31 for BW, from 0.39 to 0.38 for BP, and was constant at 0.33(0.22) for RF(WG). Predictive ability had a slight to moderate increase accumulating up to four generations of phenotypes. 1Me animals provide accurate IP, equivalent to GEBV. A minimum decay in predictive ability is observed when IP are derived from GEBV of animals from four generations back, possibly because of strong selection or the model not being completely additive.

Genomic estimated breeding values (GEBV) of genotyped animals without phenotypes can be obtained by indirect predictions (IP) using recursions on GEBV from a subset. Our objectives were to 1) evaluate the number of animals needed in recursions to maximize predictive ability, 2) assess equivalency between IP and GEBV, and 3) investigate trends in predictive ability of IP derived from recent vs. distant generations or accumulating phenotypes from recent to past generations. The number of animals (7,131) in the recursions that provided high-predictive ability was equal to the number of largest eigenvalues explaining 99% of variation in the genomic relationship matrix. IP and GEBV were equivalent (correlation ≥ 0.98). IP predictive ability was similar when recursions were based on animals from recent or distant generations; it marginally decayed with animals from four generations apart. The decline in predictive ability can be explained by strong selection or the model not being fully additive. A slight to moderate increase in IP predictive ability was observed accumulating up to four generations of phenotypes. If GEBV of animals in the subset chosen for recursions are estimated using sufficient data, animals can be from up to four generations back without significant loss in predictive ability.

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BACKGROUND: The feasibility of using coronary computed tomography angiography (CCTA) for long-term prediction of vital prognosis post-revascularization remains unknown. OBJECTIVES: To compare the prognostic value of the SYNTAX score II 2020 (SS-2020) derived from invasive coronary angiography (ICA) or CCTA in patients with three-vessel disease and/or left main coronary artery disease undergoing percutaneous or surgical revascularization. METHODS: In the SYNTAX III REVOLUTION trial, the probability of death at five years was retrospectively assessed by calculating the SS-2020 using ICA and CCTA. High- and low-risk patients for mortality were categorized according to the median percentages of predicted mortality based on both modalities. The discriminative abilities of the SS-2020 were assessed using Harrell's C statistic. RESULTS: The vital status at five years of the 215 patients revascularized percutaneously (64 patients, 29.8%) or surgically (151 patients, 70.2%) was established through national registries. In patients undergoing revascularization, the SS-2020 was possibly helpful in discriminating vital prognosis at 5 years, with similar results seen with ICA and CCTA (C-index with ICA â€‹= â€‹0.75, intercept â€‹= â€‹-0.19, slope â€‹= â€‹0.92 and C-index with CCTA â€‹= â€‹0.75, intercept â€‹= â€‹-0.22, slope â€‹= â€‹0.99). In high- and low-risk patients, Kaplan-Meier estimates showed significant, and almost identical relative differences in observed mortality, irrespective of imaging modality (ICA: 93.8% vs 78.7%, log-lank P â€‹< â€‹0.001; CCTA: 93.7% vs 78.5%, log-lank P â€‹< â€‹0.001). CONCLUSIONS: The predictive ability of the SS-2020 for five-year all-cause mortality derived from ICA and CCTA was comparable, and could helpfully discriminate vital prognosis in high- and low-risk patients.

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INTRODUCTION: Rheumatoid arthritis (RA) often involves an altered T-cell subpopulation, higher levels of inflammatory cytokines, and auto-antibodies. This study investigated whether PDCD5 could be a biomarker to predict the incidence and remission of RA so as to guide the therapeutic management of clinical RA. METHODS: One hundred fifty-two patients (41 being in both active status and stable remission status) who were newly diagnosed with RA and 38 healthy controls were enrolled. Basic clinical data were collected before using blood samples remaining in the clinic after routine complete blood count. The ability of PDCD5 and important indicators to predict the remission of RA was estimated based on receiver operating characteristic curve (ROC) analysis. RESULTS: PDCD5 expression was found to be significantly increased in RA patients in active status in comparison with healthy controls or those in stable remission status. Compared with anti-CCP, ESR and DAS28 score, PDCD5 was of better predictive value with an AUC of 0.846 (95% CI 0.780-0.912) for RA remission. The incidence risk of RA increased with higher levels of PDCD5 (OR = 1.73, 95% CI = 1.45-1.98, P = 0.005) in multiple logistic regression analysis, with the risk increasing by 2.94-times for high-risk group in comparison with low-risk group (OR = 2.94, 95% CI = 2.35-4.62, P < 0.001). The association between PDCD5 and RA remission showed a similar result. For correlation analysis, significant associations were eventually found between PDCD5 and indicated genes (FOXP3, TNF-α, IL-17A, IFN-γ and IL-6) as well as several important clinical parameters including IgG, RF, CRP, ESR, anti-CCP and DAS28 score. CONCLUSIONS: This study suggested that increased PDCD5 expression was significantly linked to the incidence and remission of RA. PDCD5 may be used as a novel biomarker for the prediction of RA incidence and remission, especially due to its potential involvement in the development of the condition.

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BACKGROUND: Increasing resilience is a priority in modern pig breeding. Recent research shows that general resilience can be quantified via variability in longitudinal data. The collection of such longitudinal data on weight, feed intake and feeding behaviour in pigs has been facilitated by the development of technologies such as automated feeding stations. The goal of this study was to investigate resilience traits, which were estimated as deviations from longitudinal weight, feed intake and feeding behaviour data during the finishing phase. A dataset with 324,207 records between the age of 95 and 155 days on 5,939 Piétrain pigs with known pedigree and genomic information was used. We provided guidelines for a rigid quality control of longitudinal body weight data, as we found that outliers can significantly affect results. Gompertz growth curve analysis, linear modelling and trajectory analyses were used for quantifying resilience traits. RESULTS: To our knowledge, this is the first study comparing resilience traits from longitudinal body weight, feed intake and feeding behaviour data in pigs. We demonstrated that the resilience traits are lowly to moderately heritable for deviations in body weight (h2 = 2.9%-20.2%), in feed intake (9.4%-23.3%) and in feeding behaviour (16.2%-28.3%). Additionally, these traits have good predictive abilities in cross-validation analyses. Deviations in individual body weight and feed intake trajectories are highly correlated (rg = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (rg = 0.39-0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvarweight), are more robust to lower observation frequencies and are repeatable over three different time periods of the finishing phase. CONCLUSIONS: Our results will help future studies investigating resilience traits and resilience-related traits. Moreover, our study provides first results on standardization of quality control and efficient data sampling from automated feeding station data. Our findings will be valuable for breeding organizations as they offer evidence that pigs' general resilience can be selected on with good accuracy. Moreover, this methodology might be extended to other species to quantify resilience based on longitudinal data.

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OBJECTIVE: The aim of this study was to identify the best anthropometric indices for predicting metabolic syndrome in US adolescents. METHODS: A cross-sectional study analyzed data of adolescents ages 10 to 19 y using the National Health and Nutrition Examination Survey 2011 to 2018 data. The receiver operating characteristic areas under the curve (AUCs) of waist circumference z score, body roundness index, body mass index, and A Body Shape Index in identifying predicting metabolic syndrome were assessed. Furthermore, sensitivity, specificity, positive predictive value, negative predictive value, and positive and negative likelihood ratios of all anthropometric indices were calculated. RESULTS: A total of 5496 adolescents were included in the analysis. Waist circumference z score had an AUC of 0.90 (95% CI, 0.89-0.91), sensitivity of 95.0% (95% CI, 89.4-98.1), and specificity of 74.8% (95% CI, 73.6, 76.0). Body roundness index had an AUC of 0.88 (95% CI, 0.87-0.89), sensitivity of 96.7% (95% CI, 91.7-99.1), and specificity of 75.2% (95% CI, 74.1-76.4). Body mass index z score had an AUC of 0.83 (95% CI, 0.81-0.85), sensitivity of 97.5% (95% CI, 92.9-99.5), and specificity of 68.2% (95% CI, 66.9-69.4). A Body Shape Index had an AUC of 0.59 (95% CI, 0.56-0.61), sensitivity of 75.0% (95% CI, 66.3-82.5), and specificity of 50.9% (95% CI, 49.5-52.2). CONCLUSIONS: Our study found waist circumference z score and body roundness index were the best predictors of predicting metabolic syndrome compared with body mass index z score and A Body Shape Index in both boys and girls. We recommend that future studies develop global cutoff points for these anthropometric indices and examine their performance in a multi-country setting.

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Proton nuclear magnetic resonance (1H NMR) spectroscopy is acknowledged as one of the most powerful analytical methods with cross-cutting applications in dairy foods. To date, the use of 1H NMR spectroscopy for the collection of milk metabolic profile is hindered by costly and time-consuming sample preparation and analysis. The present study aimed at evaluating the accuracy of mid-infrared spectroscopy (MIRS) as a rapid method for the prediction of cow milk metabolites determined through 1H NMR spectroscopy. Bulk milk (n = 72) and individual milk samples (n = 482) were analyzed through one-dimensional 1H NMR spectroscopy and MIRS. Nuclear magnetic resonance spectroscopy identified 35 milk metabolites, which were quantified in terms of relative abundance, and MIRS prediction models were developed on the same 35 milk metabolites, using partial least squares regression analysis. The best MIRS prediction models were developed for galactose-1-phosphate, glycerophosphocholine, orotate, choline, galactose, lecithin, glutamate, and lactose, with coefficient of determination in external validation from 0.58 to 0.85, and ratio of performance to deviation in external validation from 1.50 to 2.64. The remaining 27 metabolites were poorly predicted. This study represents a first attempt to predict milk metabolome. Further research is needed to specifically address whether developed prediction models may find practical application in the dairy sector, with particular regard to the screening of dairy cows' metabolic status, the quality control of dairy foods, and the identification of processed milk or incorrectly stored milk.

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BACKGROUND: There is limited knowledge on the performance of different frailty scales in clinical settings. We sought to evaluate in non-geriatric hospital departments the feasibility, agreement and predictive ability for adverse events after 1 year follow-up of several frailty assessment tools. METHODS: Longitudinal study with 667 older adults recruited from five hospitals in three different countries (Spain, Italy and United Kingdom). Participants were older than 75 years attending the emergency room, cardiology and surgery departments. Frailty scales used were Frailty Phenotype (FP), FRAIL scale, Tilburg and Groningen Frailty Indicators, and Clinical Frailty Scale (CFS). Analyses included the prevalence of frailty, degree of agreement between tools, feasibility and prognostic value for hospital readmission, worsening of disability and mortality, by tool and setting. RESULTS: Emergency Room and cardiology were the settings with the highest frailty prevalence, varying by tool between 40.4% and 67.2%; elective surgery was the one with the lowest prevalence (between 13.2% and 38.2%). The tools showed a fair to moderate agreement. FP showed the lowest feasibility, especially in urgent surgery (35.6%). FRAIL, CFS and FP predicted mortality and readmissions in several settings, but disability worsening only in cardiology. CONCLUSIONS: Frailty is a highly frequent condition in older people attending non-geriatric hospital departments. We recommend that based upon their current feasibility and predictive ability, the FRAIL scale, CFS and FP should be preferentially used in these settings. The low concordance among the tools and differences in prevalence reported and predictive ability suggest the existence of different subtypes of frailty.

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BACKGROUND AND AIMS: Cardio-metabolic diseases has been shown to be strongly associated with obesity. The aim of this study was to compare the predictive value of traditional and novel anthropometric measurement indices for cardio-metabolic diseases risk and evaluate whether new indicators can provide important information in addition to traditional indicators. METHODS AND RESULTS: China Health and Nutrition Survey (CHNS) data were obtained for this study. Baseline information for healthy participants was gathered from 1997 to 2004. The incidence of cardio-metabolic diseases was collected from 2009 to 2015 for cohort analysis. The predictive ability of each index for the risk of cardio-metabolic diseases was evaluated with time-dependent ROC analysis. Body mass index (BMI) showed the greatest predictive ability for cardio-metabolic disease incidence among all traditional and novel indices (Harrell's C statistic (95% CI): 0.7386 (0.7266-0.7507) for hypertension, 0.7496 (0.7285-0.7706) for diabetes, 0.7895 (0.7593-0.8196) for stroke and 0.7581 (0.7193-0.7969) for myocardial infarction). The addition of novel indices separately into the BMI model did not improve the predictive ability. Novel anthropometric measurement indices such as a body shape index (ABSI), abdominal volume index (AVI) and triponderal mass index (TMI), had a certain prediction ability for adults with BMI <24 kg/m2 compared to those with BMI ≥24 kg/m2. CONCLUSION: No strong evidence supports novel anthropometric measurement indices were better than BMI in the prediction of cardio-metabolic diseases incidence among Chinese adults. Novel anthropometric measurement indices, mainly for abdominal obesity, may have a high predictive effect for adults with BMI <24 kg/m2.

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