Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

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.

Genome-enabled prediction of indicator traits of resistance to gastrointestinal nematodes in sheep using parametric models and artificial neural networks

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.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.)

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.

PDCD5 as a Potential Biomarker for Improved Prediction of the Incidence and Remission for Patients with Rheumatoid Arthritis

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.

The ability of computed tomography angiography to predict 5-year mortality in the SYNTAX III REVOLUTION trial

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 ​= ​0.75, intercept ​= ​-0.19, slope ​= ​0.92 and C-index with CCTA ​= ​0.75, intercept ​= ​-0.22, slope ​= ​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 ​< ​0.001; CCTA: 93.7% vs 78.5%, log-lank P ​< ​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.

Predictive ability of anthropometric indices in identifying metabolic syndrome among US adolescents 10 to 19 years old: Analysis from the National Health and Nutrition Examination Survey 2011 to 2018 data set

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.

A promising resilience parameter for breeding: the use of weight and feed trajectories in growing pigs

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 (r_g = 0.78) with low to moderate favourable genetic correlations with feed conversion ratio (r_g = 0.39-0.49). Lastly, we showed that some resilience traits, such as the natural logarithm of variances of observed versus predicted body weights (lnvar_weight), 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.

Predictive ability of traditional and novel anthropometric measurement indices for cardio-metabolic diseases in Chinese adults: China Health and Nutrition Survey (CHNS) cohort study

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.

Are people good prognosticators of their own pain? An exploration of the relationship between sex-specific pain beliefs and clinical pain evaluation

BACKGROUND

Under-explored to date are the interacting influences of patient sex on multi-modal evaluation techniques that tap different domains of the pain experience.

OBJECTIVES

The primary aim of Study 1 was to explore the accuracy of sex-specific personal pain beliefs in relation to quantitative pain indicators within sexes, and the secondary objective was to compare the accuracy of sex-specific personal pain beliefs in relation to quantitative pain indicators between sexes. The primary objective of Study 2 was to explore the accuracy of sex-specific personal pain beliefs and self-rated pain severity within sexes, and the secondary objective was to compare sex-specific personal pain beliefs and pain severity ratings between sexes.

METHODS

A cross-sectional analysis on two datasets was performed (Study 1, n = 50; Study 2, n = 111). For both studies, independent samples t-tests were used to identify differences in clinical pain evaluations based on sex-specific pain beliefs. Receiver Operating Characteristic (ROC) curves were used to compare the predictive accuracy of males and females clinical pain evaluations based on their ability to handle pain.

RESULTS

There were no statistically significant differences in clinical pain evaluations based on self-rated pain beliefs in either study. In Study 2, males were descriptively more accurate predictors of their clinical pain evaluations than were females, though none of the between sex comparisons were statistically significant.

CONCLUSION

This work highlights the importance of considering all available clinical pain evaluations as one technique is unlikely to represent the patients pain experience.

Systematic review of the utility of the frailty index and frailty phenotype to predict all-cause mortality in older people

BACKGROUND

Current guidelines for healthcare of community-dwelling older people advocate screening for frailty to predict adverse health outcomes, but there is no consensus on the optimum instrument to use in such settings. The objective of this systematic review of population studies was to compare the ability of the frailty index (FI) and frailty phenotype (FP) instruments to predict all-cause mortality in older people.

METHODS

Studies published before 27 July 2022 were identified using Ovid MEDLINE, Embase, Scopus, Web of Science and CINAHL databases. The eligibility criteria were population-based prospective studies of community-dwelling older adults (aged 65 years or older) and evaluation of both the FI and FP for prediction of all-cause mortality. The Scottish Intercollegiate Guidelines Network's Methodology checklist was used to assess study quality. The areas under the receiver operator characteristic curves (AUC) were compared, and the proportions of included studies that achieved acceptable discriminatory power (AUC>0.7) were calculated for each frailty instrument. The results were stratified by the use of continuous or categorical formats of each instrument. The review was reported in accordance with the PRISMA and SWiM guidelines.

RESULTS

Among 8 studies (range: 909 to 7713 participants), both FI and FP had comparable predictive power for all-cause mortality. The AUC values ranged from 0.66 to 0.84 for FI continuous, 0.60 to 0.80 for FI categorical, 0.63 to 0.80 for FP continuous and 0.57 to 0.79 for FP categorical. The proportion of studies achieving acceptable discriminatory power were 75%, 50%, 63%, and 50%, respectively. The predictive ability of each frailty instrument was unaltered by the number of included items.

CONCLUSIONS

Despite differences in their content, both the FI and FP instruments had modest but comparable ability to predict all-cause mortality. The use of continuous rather than categorical formats in either instrument enhanced their ability to predict all-cause mortality.

Genetic evaluation of age at first calving and days open under different censoring scenarios in Iranian Holstein cows

The present study aimed to investigate the effect of censoring, the situations in which incomplete at the time, out of range, and/or delayed records were considered, in the estimation of genetic parameters for age at first calving (AFC) and days open (DO) in Iranian Holstein cows. The dataset included 281,772 records collected from 1991 to 2019 by the Vahdat Cooperative Company, a pioneer dairy farm in Isfahan Province, the central part of Iran. Five animal models including linear model (LM), penalty model (PM), modified penalty model (MPM), linear-threshold model (LTM), and modified linear-threshold model (MLTM) were used for genetic evaluation of the trait studied. The predictive ability of the models was assessed using cross-validation. The lowest mean square of error and highest r(y,y) were obtained under MLTM for AFC and under LTM for DO, indicating that MLTM and LTM are recommended for genetic evaluation of AFC and DO with censored records in Iranian Holstein cows, respectively. The prediction accuracy of the models for AFC was relatively similar, ranging from 0.46 (under LM) to 0.48 (under PM, LTM, and MLTM). For DO, prediction accuracy values ranged from 0.36 (under LM) to 0.47 (under PM and LTM). The posterior mean for heritability of AFC under MLTM was 0.11. There was no significant difference among posterior means for the heritability of AFC under different models. Therefore, LM is preferred for genetic evaluation of AFC in Iranian Holsteins, and taking censored records into account is unnecessary. The posterior mean for heritability of DO under LTM was 0.09. There were no statistically significant differences among the heritability estimates of DO under LTM, PM, and MLTM. But considering censored records for genetic evaluation of DO affects the estimation of heritability and improved model accuracy for this trait. Therefore, LTM is preferred and recommended for genetic evaluation of DO in Iranian Holsteins.

Physical performance measures in frailty screening: diagnostic and prognostic accuracy in the Toledo Study of Healthy Ageing

INTRODUCTION

The present study aimed to explore the diagnostic and prognostic accuracy of standard and population-specific Physical Performance Measures (PPMs) cut-off points for frailty screening.

DESIGN

Prospective cohort study.

SETTING AND PARTICIPANTS

Population-based study including 2328 subjects from the Toledo Study of Healthy Aging (age = 76.37 ± 6.78). Data related to frailty status and PPMs was collected at baseline visit (2011-2013). Mortality and hospitalization were ascertained up to March 2019 and December 2017, respectively, whereas disability onset and worsening were evaluated in the 2015-2017 visit.

METHODS

Gait speed and Short Physical Performance Battery population-specific cut-off points for frailty were computed using receiver operating characteristics (ROC) curve analysis. Head-to-head comparison of associations with adverse events against existing reference values (SPPB≤6, GS < 0.8 m/s) and classical (Frailty Phenotype, Frailty Index) and newly incorporated frailty tools (12- and 5-item Frailty Trait Scale) were explored through logistic and Cox regressions. Predictive ability was compared through areas under the curves (AUCs) for disability onset/worsening and integrated AUCs for mortality and hospitalization (time-censoring adverse events).

RESULTS

PPMs population-specific cut-off points (SPPB ≤7 and GS ≤ 0.75 m/s for males; SPPB ≤4 and GS ≤ 0.5 for females) outperformed published reference thresholds in terms of diagnostic accuracy. Frailty identified through PPMs was associated with adverse events (death, hospitalization and incident disability) similarly to that assessed using the newly incorporated tools and showed similar prognostic accuracy (mortality [IAUCs≈0.7], hospitalization [IAUCs≈0.8] and disability onset/worsening [AUCs≈0.62]), except for the tool used to assess frailty.

CONCLUSIONS

Our results suggest that PPMs might serve as the first screen to identify candidates for further frailty assessment and exploration of underlying mechanisms, allowing opportunistic on-time screening in different settings (community and primary care) in which frailty instruments are rarely implementable.

The stability of the Bayley scales in early childhood and its relationship with future intellectual abilities in a low to middle income country

BACKGROUND

The Bayley Scales of Infant and Toddler Development is widely used worldwide. The objective of the current study was to measure the stability of the Bayley Scales during early childhood and its relationship with intellectual abilities at four years in young Nepalese children.

METHODS

In a prospective cohort we used the Bayley 3rd edition to measure early child development in 529 Nepalese children at 6-11, 18-23 and 30-35 months. At four years, we used the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) to measure intellectual abilities. We expressed the stability of the Bayley scores by intraclass correlation coefficients (ICCs) and concordance correlation coefficients (CCCs). The relationship between the Bayley scores and the WPPSI full-scale IQ (FSIQ) at four years was examined in regression models.

RESULTS

The ICCs between the Bayley scores across timepoints were 0.01 (95 % CI -0.06, 0.04), 0.19 (95 % CI 0.15, 0.26) and 0.22 (95 % CI 0.17, 0.28) for the Cognitive, Language and Motor composite scores. The CCC for the composite scores ranged from 0.05 to 0.20 between 6 and 11 and 30-35 months and from 0.20 to 0.36 between 18 and 23 and 30-35 months. The Bayley scores at 6-11, 18-23 and 30-35 months explained 3 %, 20 % and 36 % of the variation of the FSIQ.

CONCLUSION

The stability of the Bayley scales is poor in early childhood, and its relationship with future intellectual abilities is poor in infancy but improves slightly with age in early childhood. Findings from this large community-based cohort of healthy at-risk children are relevant when measuring early child development worldwide.

Predictive ability of the G8 screening test to determine probable sarcopenia and abnormal comprehensive geriatric assessment in older patients with solid malignancies

Background

Pre-treatment evaluation for sarcopenia is recommended in cancer patients. New screening tests that are less time-consuming and can identify patients who will potentially benefit from geriatric assessment are being developed; the G8 geriatric screening test is one such example. We aimed to investigate whether the G8 screening test can detect probable sarcopenia and is valid and reliable compared to a comprehensive geriatric assessment (CGA) in Turkish older adults with solid cancers.

Methods

We included solid cancer patients referred to a single center. Probable sarcopenia and abnormal CGA were defined as low handgrip strength. Cut-offs for handgrip strength in the Turkish population have been previously determined to be 32 kg for males and 22 kg for females and impairment in at least one of the CGA tests, respectively. The CGA tests comprised KATZ Basic Activities of Daily Living Scale Lawton–Brody Instrumental Activities of Daily Living Scale, Mini-Mental-State Examination Scale, Geriatric Depression Scale-15, and Mini-Nutritional Assessment Short Form. Receiver operating characteristic curve analyses evaluated the test’s predictive ability. Intra-rater and inter-rater reliabilities were assessed.

Results

The median age of the 76 patients included was 72 (65–91) years. There was a moderate correlation between handgrip strength and the G8 test total score. The sensitivity and specificity of the G8 test to detect probable sarcopenia alone (cut off score = 12.5) were 50 and 92%, respectively (AUC: 0.747; p < 0.001); to determine abnormal CGA plus probable sarcopenia (cut off score = 13) were 93.33 and 86.89%, respectively (AUC: 0.939; p < 0.001); and to detect abnormal CGA alone (cut off score = 14) were 79.63 and 95.45%, respectively (AUC: 0.893; p < 0.001). The G8 test results agreed with those of CGA (κ = 0.638; p < 0.001). Both inter- and intra-rater assessments of G8 scores revealed a strong agreement (Interclass correlation coefficient = 0.979, p < 0.001 and ρ = 0.994, p < 0.001, respectively).

Conclusions

The Turkish version of the G8 test is a good screening tool to detect probable sarcopenia alone and in conjunction with abnormal CGA in older patients with solid malignancies. The G8 screening tool may thus be useful in detecting probable sarcopenia in Turkish older adults with solid cancers.

Role of handgrip strength in predicting new-onset diabetes: findings from the survey of health, ageing and retirement in Europe

BACKGROUND

Diabetes is a major concern for the global health burden. This study aimed to investigate the relationship between handgrip strength (HGS) and the risk of new-onset diabetes and to compare the predictive abilities between relative HGS and dominant HGS.

METHODS

This longitudinal study used data from the Survey of Health, Ageing and Retirement in Europe (SHARE), including 66,100 European participants aged 50 years or older free of diabetes at baseline. The Cox proportional hazard model was used to analyze the relationship between HGS and diabetes, and the Harrell's C index, net reclassification index (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the predictive abilities of different HGS expressions.

RESULTS

There were 5,661 diabetes events occurred during follow-up. Compared with individuals with lowest quartiles, the hazard ratios (95 % confidence intervals) of the 2nd-4th quartiles were 0.88 (0.81-0.94), 0.82 (0.76-0.89) and 0.85 (0.78-0.93) for dominant HGS, and 0.95 (0.88-1.02), 0.82 (0.76-0.89) and 0.60 (0.54-0.67) for relative HGS. After adding dominant HGS to an office-based risk score (including age, gender, body mass index, smoking, and hypertension), the incremental values of the Harrell's C index, NRI, IDI of relative HGS were all slightly higher than those of dominant HGS in both training and validation sets.

CONCLUSIONS

Our findings supported that HGS was an independent predictor of new-onset diabetes in the middle-aged and older European population. Moreover, relative HGS exhibited a slightly higher predictive ability than dominant HGS.

Plasma fibrinogen acts as a predictive factor for pathological complete response to neoadjuvant chemotherapy in breast cancer: a retrospective study of 1004 Chinese breast cancer patients

Background

The aim of this study was to evaluate the relationship between pre-treatment plasma fibrinogen (Fib) level and pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer patients and to assess the role of plasma Fib as a predictive factor.

Methods

Data from 1004 consecutive patients with invasive breast cancer who received NAC and subsequent surgery were retrospectively analysed. Both univariate and multivariate analyses based on logistic regression model were performed to identify clinicopathological factors associated with pCR to NAC. Cox regression model was used to determine the correlation between clinical or pathological parameters and recurrence-free survival (RFS). The Kaplan-Meier method and the log-rank test were applied in the survival analysis.

Results

The median value of Fib, rather than other plasma coagulation parameters, was significantly increased in non-pCR patients compared with pCR patients (P = 0.002). Based on the cut-off value estimated by the receiver operating characteristic (ROC) curve analysis, patients were divided into low or high Fib groups (Fib < 3.435 g/L or ≥ 3.435 g/L). Low Fib levels were significantly associated with premenopausal or perimenopausal status (P <  0.001), tumour size ≤5 cm (P = 0.002), and positive hormone receptor status (P = 0.002). After adjusted for other clinicopathological factors in the multivariate logistic regression model, low Fib status was strongly associated with pCR to NAC (OR = 3.038, 95% CI 1.667–5.537, P <  0.001). Survival analysis showed that patients with low Fib levels exhibited better 3-year RFS compared with patients with high Fib levels in the tumour size>5 cm group (77.5% vs 58.4%, log-rank, P = 0.0168).

Conclusions

This study demonstrates that low pre-treatment plasma Fib (Fib < 3.435 g/L) is an independent predictive factor for pCR to NAC in breast cancer patients. Moreover, T3-featured breast cancer patients with lower Fib level exhibit better RFS outcomes after NAC compared with high Fib status.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08284-8.

Load-velocity relationship in the horizontal leg-press exercise in older women and men

This study analyzed the predictive ability of movement velocity to estimate the relative load (i.e., % of one-repetition maximum [1RM]) during the horizontal leg-press exercise in older women and men. Twenty-four women and fourteen men living in community-dwelling centers volunteered to participate in this study. All participants performed a progressive loading test up to 1RM in the horizontal leg-press. The fastest peak velocity (PV) and mean velocity (MV) attained with each weight were collected for analysis. Linear regression equations were modeled for women and men. We observed very strong linear relationships between both velocity variables and the relative load in the horizontal leg-press in women (PV: r² = 0.93 and standard error of the estimate (SEE) = 5.96% 1RM; MV: r² = 0.94 and SEE = 5.59% 1RM) and men (PV: r² = 0.93 and SEE = 5.96% 1RM; MV: r² = 0.94 and SEE = 5.97% 1RM). The actual 1RM and the estimated 1RM using both the PV and MV presented trivial differences and very strong relationships (r = 0.98-0.99) in both sexes. Men presented significantly higher (p < 0.001-0.05) estimated PV and MV against all relative loads compared to women (average PV = 0.81 vs. 0.69 m·s^-1 and average MV = 0.44 vs. 0.38 m·s^-1). Our data suggest that movement velocity accurately estimates the relative load during the horizontal leg-press in older women and men. Coaches and researchers can use the proposed sex-specific regression equations in the horizontal leg-press to implement velocity-monitored resistance training with older adults.

Simple Two-Dimensional Echocardiographic Scoring System for the Estimation of Left Ventricular Filling Pressure

BACKGROUND

When left ventricular filling pressure (LVFP) increases, the mitral valve opens early and precedes tricuspid valve opening in early diastole. The authors hypothesized that a visually assessed time sequence of atrioventricular valve opening could become a new marker of elevated LVFP. The aim of this study was to test the diagnostic ability of a novel echocardiographic scoring system, the visually assessed time difference between mitral valve and tricuspid valve opening (VMT) score, in patients with heart failure.

METHODS

One hundred nineteen consecutive patients who underwent cardiac catheterization within 24 hours of echocardiographic examination were retrospectively analyzed as a derivation cohort. In addition, a prospective study was conducted to validate the diagnostic ability of the VMT score in 50 patients. Elevated LVFP was defined as mean pulmonary artery wedge pressure (PAWP) ≥ 15 mm Hg. The time sequence of atrioventricular valve opening was visually assessed and scored (0 = tricuspid valve first, 1 = simultaneous, 2 = mitral valve first). When the inferior vena cava was dilated, 1 point was added, and VMT score was ultimately graded as 0 to 3. Cardiac events were recorded for 1 year after echocardiography.

RESULTS

In the derivation cohort, PAWP was elevated with higher VMT scores (score 0, 10 ± 5; score 1, 12 ± 4; score 2, 22 ± 8; score 3, 28 ± 4 mm Hg; P < .001, analysis of variance). VMT score ≥ 2 predicted elevated PAWP with accuracy of 86% and showed incremental predictive value over clinical variables and guideline-recommended diastolic function grading. These observations were confirmed in the prospective validation cohort. Importantly, VMT score ≥ 2 discriminated elevated PAWP with accuracy of 82% in 33 patients with monophasic left ventricular inflow in the derivation cohort. Kaplan-Meier analysis demonstrated that patients with VMT scores ≥ 2 were at higher risk for cardiac events than those with VMT scores ≤ 1 (P < .001).

CONCLUSIONS

VMT scoring could be a novel additive marker of elevated LVFP and might also be associated with adverse outcomes in patients with heart failure.

Genomic predictions for fillet yield and firmness in rainbow trout using reduced-density SNP panels

Background

One of the most important goals for the rainbow trout aquaculture industry is to improve fillet yield and fillet quality. Previously, we showed that a 50 K transcribed-SNP chip can be used to detect quantitative trait loci (QTL) associated with fillet yield and fillet firmness. In this study, data from 1568 fish genotyped for the 50 K transcribed-SNP chip and ~ 774 fish phenotyped for fillet yield and fillet firmness were used in a single-step genomic BLUP (ssGBLUP) model to compute the genomic estimated breeding values (GEBV). In addition, pedigree-based best linear unbiased prediction (PBLUP) was used to calculate traditional, family-based estimated breeding values (EBV).

Results

The genomic predictions outperformed the traditional EBV by 35% for fillet yield and 42% for fillet firmness. The predictive ability for fillet yield and fillet firmness was 0.19–0.20 with PBLUP, and 0.27 with ssGBLUP. Additionally, reducing SNP panel densities indicated that using 500–800 SNPs in genomic predictions still provides predictive abilities higher than PBLUP.

Conclusion

These results suggest that genomic evaluation is a feasible strategy to identify and select fish with superior genetic merit within rainbow trout families, even with low-density SNP panels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07404-9.

[Predictive ability of risk assessment tool regarding HIV infection for men who have sex with men]

Objective: To analyze the predictive ability of HIV infection risk assessment tool for men who have sex with men (MSM). Sentinel surveillance data of MSM in Taizhou prefecture of Zhejiang province was used. Methods: MSM involved in AIDS sentinel surveillance program in Taizhou from 2013 to 2017 were included in the study and items listed in the HIV infection risk assessment tool for MSM was revised. Related data on questions and options involved in sentinel surveillance was collected and individual risk scores were calculated. We determined the predictive ability of this tool by comprehensive analyzing the HIV infection status and individual risk scores. Results: A total of 1 944 MSM were included in the study, with an average age of (35.04±13.28)years old. Most of them were recruited from the venues (55.7%) and 48.2% were never married. Among these MSM, HIV infection rate was 12.6%(245/1 944) with the median of risk score as 23.99, versus 20.36 from the HIV negative ones. Significant differences appeared on the risk scores between the target populations that with different HIV status (Mann-Whitney test, P=0.007). According to the principle of decision tree, MSM were divided into two groups according to risk scores: ≤18.66 and >18.66. It appeared that the risk scores were in parallel with the rates of HIV infection (χ(2)=13.102, P<0.001). Results from the multivariate analysis showed that MSM with higher risk score were more likely to be infected with HIV (>18.66 vs. ≤18.66: aOR=1.72, 95%CI: 1.27-2.32, P<0.001). Area under the ROC curve (AUC) for HIV infection was 0.553 (95%CI: 0.516-0.590, P=0.007). At the point of risk score 19.01, Youden's index appeared the maximum, with sensitivity as 0.69 and specificity as 0.43, of this tool. Conclusions: The HIV infection risk assessment tool for MSM developed based on Delphi method can predict the risk of HIV infection in MSM to some extent. MSM with higher risk score seemed likely to be infected with HIV. Items of this tool need to be adjusted for the verification of the tool through cohort studies in the near future.

Study protocol for prospect trial to elucidate the utility of echocardiography-based cardiac output in acute heart failure (PREDICT)

BACKGROUND

Early worsening heart failure (WHF), defined as worsening of symptoms and signs of heart failure requiring intensification of medical or mechanical therapy during an admission for acute decompensated heart failure (ADHF), has recently been recognized as a risk of morbidity or mortality after the discharge. Although echocardiographic parameters of left ventricular (LV) output has been shown to be associated with long-term outcome in heart failure patients, its predictive value for early WHF has not been elucidated.

METHODS

Prospect trial to Elucidate the utility of EchocarDiography-based Cardiac output in acute heart failure (PREDICT) is a multicenter, nonrandomized, prospective observational study to test the predictive value of echocardiographic LV output parameters for early WHF in ADHF patients. We will enroll patients admitted to 16 participating hospitals due to ADHF who did not receive positive inotropic agents as an initial therapy. Primary outcome will be set at early WHF defined as need for initiation of positive inotropic agents within 7 days after the admission. Predictive accuracy will be compared between Doppler echocardiographic LV output (stroke distance, stroke volume index, and cardiac index) and low perfusion findings assessed by physical examination.

RESULTS

N/A.

CONCLUSIONS

The PREDICT is expected to provide large data set to test the predictive value of echocardiographic LV output for early WHF in ADHF patients.

Validation and evaluation of clinical prediction systems for first and repeated transarterial chemoembolization in unresectable hepatocellular carcinoma: A Chinese multicenter retrospective study

BACKGROUND

The treatment outcome of transarterial chemoembolization (TACE) in unresectable hepatocellular carcinoma (HCC) varies greatly due to the clinical heterogeneity of the patients. Therefore, several prognostic systems have been proposed for risk stratification and candidate identification for first TACE and repeated TACE (re-TACE).

AIM

To investigate the correlations between prognostic systems and radiological response, compare the predictive abilities, and integrate them in sequence for outcome prediction.

METHODS

This nationwide multicenter retrospective cohort consisted of 1107 unresectable HCC patients in 15 Chinese tertiary hospitals from January 2010 to May 2016. The Hepatoma Arterial-embolization Prognostic (HAP) score system and its modified versions (mHAP, mHAP2 and mHAP3), as well as the six-and-twelve criteria were compared in terms of their correlations with radiological response and overall survival (OS) prediction for first TACE. The same analyses were conducted in 912 patients receiving re-TACE to evaluate the ART (assessment for re-treatment with TACE) and ABCR (alpha-fetoprotein, Barcelona Clinic Liver Cancer, Child-Pugh and Response) systems for post re-TACE survival (PRTS).

RESULTS

All the prognostic systems were correlated with radiological response achieved by first TACE, and the six-and-twelve criteria exhibited the highest correlation (Spearman R = 0.39, P = 0.026) and consistency (Kappa = 0.14, P = 0.019), with optimal performance by area under the receiver operating characteristic curve of 0.71 [95% confidence interval (CI): 0.68-0.74]. With regard to the prediction of OS, the mHAP3 system identified patients with a favorable outcome with the highest concordance (C)-index of 0.60 (95%CI: 0.57-0.62) and the best area under the receiver operating characteristic curve at any time point during follow-up; whereas, PRTS was well-predicted by the ABCR system with a C-index of 0.61 (95%CI: 0.59-0.63), rather than ART. Finally, combining the mHAP3 and ABCR systems identified candidates suitable for TACE with an improved median PRTS of 36.6 mo, compared with non-candidates with a median PRTS of 20.0 mo (log-rank test P < 0.001).

CONCLUSION

Radiological response to TACE is closely associated with tumor burden, but superior prognostic prediction could be achieved with the combination of mHAP3 and ABCR in patients with unresectable liver-confined HCC.

Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information

Background

Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding.

Results

Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55–0.75 when using the documented pedigree and 0.61–0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37–115% and 13–77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively.

Conclusion

It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

Genomic selection for non-key traits in radiata pine when the documented pedigree is corrected using DNA marker information

BACKGROUND

Non-key traits (NKTs) in radiata pine (Pinus radiata D. Don) refer to traits other than growth, wood density and stiffness, but still of interest to breeders. Branch-cluster frequency, stem straightness, external resin bleeding and internal checking are examples of such traits and are targeted for improvement in radiata pine research programmes. Genomic selection can be conducted before the performance of selection candidates is available so that generation intervals can be reduced. Radiata pine is a species with a long generation interval, which if reduced could significantly increase genetic gain per unit of time. The aim of this study was to evaluate the accuracy and predictive ability of genomic selection and its efficiency over traditional forward selection in radiata pine for the following NKTs: branch-cluster frequency, stem straightness, internal checking, and external resin bleeding.

RESULTS

Nine hundred and eighty-eight individuals were genotyped using exome capture genotyping by sequencing (GBS) and 67,168 single nucleotide polymorphisms (SNPs) used to develop genomic estimated breeding values (GEBVs) with genomic best linear unbiased prediction (GBLUP). The documented pedigree was corrected using a subset of 704 SNPs. The percentage of trio parentage confirmed was about 49% and about 50% of parents were re-assigned. The accuracy of GEBVs was 0.55-0.75 when using the documented pedigree and 0.61-0.80 when using the SNP-corrected pedigree. A higher percentage of additive genetic variance was explained and a higher predictive ability was observed when using the SNP-corrected pedigree than using the documented pedigree. With the documented pedigree, genomic selection was similar to traditional forward selection when assuming a generation interval of 17 years, but worse than traditional forward selection when assuming a generation interval of 14 years. After the pedigree was corrected, genomic selection led to 37-115% and 13-77% additional genetic gain over traditional forward selection when generation intervals of 17 years and 14 years were assumed, respectively.

CONCLUSION

It was concluded that genomic selection with a pedigree corrected by SNP information was an efficient way of improving non-key traits in radiata pine breeding.

Evaluation of Genomic Selection for Seven Economic Traits in Yellow Drum (Nibea albiflora)

Yellow drum (Nibea albiflora) is an important maricultural fish in China, and genetic improvement is necessary for this species. This research evaluated the application of genomic selection methods to predict the genetic values of seven economic traits for yellow drum. Using genome-wide single-nucleotide polymorphisms (SNPs), we estimated the genetic parameters for seven traits, including body length (BL), swimming bladder index (SBI), swimming bladder weight (SBW), body thickness (BT), body height (BH), body length/body height ratio (LHR), and gonad weight index (GWI). The heritability estimates ranged from 0.309 to 0.843. We evaluated the prediction performance of various statistical methods, and no one method provided the highest predictive ability for all traits. We then evaluated and compared the use of genome-wide association study (GWAS)-informative SNPs and random SNPs for prediction and found that GWAS-informative SNPs obviously increased. It only needed 5 and 100 informative SNPs for LHR and BT to achieve almost the same predictive abilities as using genome-wide SNPs, and for BL, SBI, SBW, BH, and GWI, about 1000 to 3000 informative SNPs were needed to achieve whole-genome level predictive abilities. It can be concluded from the test results that breeders can use fewer SNPs to save the breeding costs of genomic selection for some traits.

Association between separate items of the Mini Nutritional Assessment instrument and mortality among older adults: A prospective cohort study introducing a trimmed MNA version

BACKGROUND & AIMS

The predictive ability of the separate items of the full Mini Nutritional Assessment (MNA-F) instrument in relation to all-cause mortality (ACM) in older adults is unclear. The present study aimed to examine the magnitudes of the independent associations between separate MNA-F items and ACM among people aged ≥65 years old admitted to hospital. A secondary aim was to construct a trimmed MNA instrument (MNA-T) consisting of MNA-F items that contributed independently to predicting ACM, and compare its predictive ability with that of MNA-F and MNA short forms (MNA-SF).

METHODS

This prospective cohort study included 1768 individuals aged ≥65 years old admitted to a Swedish hospital who answered MNA-F in 2008. They were followed-up for ACM after two years using national registers. Associations with ACM were calculated using Cox regression analysis. Predictive abilities for ACM were calculated using an R²-type measure.

RESULTS

After a mean follow-up time of 1.7 years (3006 person-years), 455 (25.7%) participants had died. In adjusted analyses, nine MNA-F items contributed independently to predicting ACM (P < 0.20) and were included in MNA-T. Five items (food intake, independent living, > 3 prescription drugs, fluid intake, self-assessed health status) had significant contributions (P < 0.05). MNA-T had a better predictive ability for ACM than MNA-F and MNA-SF.

CONCLUSIONS

MNA-F could possibly benefit from excluding less important and more time-consuming items when used for predicting the ultimate consequence of malnutrition, an early death. MNA-T should be a more reliable instrument to use for this purpose.

Very early MRI responses to therapy as a predictor of later radiographic progression in early rheumatoid arthritis

Background

The objective of this study was to evaluate early changes in magnetic resonance imaging (MRI) and clinical disease activity measures as predictors of later structural progression in early rheumatoid arthritis (RA).

Methods

This was a post hoc analysis of data pooled across treatments from a three-arm (tofacitinib monotherapy, tofacitinib with methotrexate [MTX], or MTX monotherapy) trial of MTX-naïve patients with early, active RA. Synovitis, osteitis and erosions were assessed with the Outcome Measures in Rheumatology (OMERACT) RA MRI scoring system (RAMRIS) and RAMRIQ (automated quantitative RA MRI assessment system; automated RAMRIS) at months 0, 1, 3, 6 and 12. Radiographs were assessed at months 0, 6 and 12, and clinical endpoints were assessed at all timepoints. Univariate and multivariate analyses explored the predictive value of early changes in RAMRIS/RAMRIQ parameters and disease activity measures, with respect to subsequent radiographic progression.

Results

Data from 109 patients with a mean RA duration of 0.7 years were included. In univariate analyses, changes in RAMRIS erosions at months 1 and 3 significantly predicted radiographic progression at month 12 (both p <  0.01); changes in RAMRIQ synovitis and osteitis at months 1 and 3 were significant predictors of RAMRIS erosions and radiographic progression at month 12 (all p <  0.01). In subsequent multivariate analyses, RAMRIS erosion change at month 1 (p <  0.05) and RAMRIQ osteitis changes at months 1 and 3 (both p <  0.01) were significant independent predictors of radiographic progression at month 12. Univariate analyses demonstrated that changes in Clinical Disease Activity Index (CDAI) and Disease Activity Score in 28 joints, erythrocyte sedimentation rate (DAS28-4[ESR]) at months 1 and 3 were not predictive of month 12 radiographic progression.

Conclusions

MRI changes seen as early as 1 month after RA treatment initiation have the potential to better predict long-term radiographic progression than changes in disease activity measures.

Trial registration

ClinicalTrials.gov, NCT01164579.

Correlation between invasive microbiota in margin-surrounding mucosa and anastomotic healing in patients with colorectal cancer

BACKGROUND

Impaired anastomotic healing is one of the major complications resulting from radical resection in colorectal cancer (CRC). Accumulating evidence suggests that intestinal microbiota is correlated with anastomotic healing.

AIM

To explore the microbiota structural shift in margin-surrounding mucosa and evaluate the predictive ability of selected bacterial taxa for impaired anastomotic healing.

METHODS

Margin-surrounding mucosa samples derived from 37 patients were collected to characterize the microbial community structure by 16s rRNA gene sequencing. The patients were divided into two groups according to the healing status of anastomoses: well-healing group (n = 30) and impaired-healing group (n = 7). Statistic differences in bacteria taxa were compared by Wilcoxon test and chi-squared test. The predictive ability of the selected bacterial taxa for the healing status of anastomoses was evaluated by the area under the receiver operator characteristic curve.

RESULTS

Community structure shifts were observed in the impaired-healing group and well-healing group. Six bacterial species were found to be significantly correlated with anastomotic healing, and among these species, Alistipes shahii, Dialister pneumosintes, and Corynebacterium suicordis were considered as the predictive factors. Taking the known risk factor age into consideration, Alistipes shahii, Dialister pneumosintes, and Corynebacterium suicordis improved predictive ability for the healing status of anastomoses.

CONCLUSION

These data show that Alistipes shahii, Dialister pneumosintes, and Corynebacterium suicordis could be considered as supplementary factors in the prediction of anastomosis healing status in patients after CRC radical resection.

Pea genomic selection for Italian environments

BACKGROUND

A thorough verification of the ability of genomic selection (GS) to predict estimated breeding values for pea (Pisum sativum L.) grain yield is pending. Prediction for different environments (inter-environment prediction) has key importance when breeding for target environments featuring high genotype × environment interaction (GEI). The interest of GS would increase if it could display acceptable prediction accuracies in different environments also for germplasm that was not used in model training (inter-population prediction).

RESULTS

Some 306 genotypes belonging to three connected RIL populations derived from paired crosses between elite cultivars were genotyped through genotyping-by-sequencing and phenotyped for grain yield, onset of flowering, lodging susceptibility, seed weight and winter plant survival in three autumn-sown environments of northern or central Italy. The large GEI for grain yield and its pattern (implying larger variation across years than sites mainly due to year-to-year variability for low winter temperatures) encouraged the breeding for wide adaptation. Wider within-population than between-population variation was observed for nearly all traits, supporting GS application to many lines of relatively few elite RIL populations. Bayesian Lasso without structure imputation and 1% maximum genotype missing rate (including 6058 polymorphic SNP markers) was selected for GS modelling after assessing different GS models and data configurations. On average, inter-environment predictive ability using intra-population predictions reached 0.30 for yield, 0.65 for onset of flowering, 0.64 for seed weight, and 0.28 for lodging susceptibility. Using inter-population instead of intra-population predictions reduced the inter-environment predictive ability to 0.19 for grain yield, 0.40 for onset of flowering, 0.28 for seed weight, and 0.22 for lodging susceptibility. A comparison of GS vs phenotypic selection (PS) based on predicted genetic gains per unit time for same selection costs suggested greater efficiency of GS for all traits under various selection scenarios. For yield, the advantage in predicted efficiency of GS over PS was at least 80% using intra-population predictions and 20% using inter-population predictions. A genome-wide association study confirmed the highly polygenic control of most traits.

CONCLUSIONS

Genome-enabled predictions can increase the efficiency of pea line selection for wide adaptation to Italian environments relative to phenotypic selection.

Predicting repeated self-harm or suicide in adolescents and young adults using risk assessment scales/tools: a systematic review protocol

BACKGROUND

Self-harm and suicide have been identified as serious public health problems in children, adolescents, and young people across the world. Suicide is a major cause of mortality in this population and is commonly preceded by self-harm. Both suicide and self-harm are difficult to predict, and several risk scales and tools are in use for this purpose. Currently, there is only a small amount of evidence available regarding their predictive ability in clinical practice, and no consensus as to which is the most suitable for particular populations or settings. The aim of this review is to evaluate the ability of risk scales to predict future episodes of suicide or self-harm in adolescents and young adults presenting to clinical services with attempted suicide or an episode of self-harm.

METHODS

A comprehensive search of electronic databases (MEDLINE, EMBASE, CINAHL, and PsycINFO) from inception will be conducted to identify studies that look at the ability of risk scales to predict suicide or future episodes of self-harm in adolescents and young adults presenting to clinical services with attempted suicide or an episode of self-harm. Two authors will independently carry out key methodological steps such as study screening and selection and data extraction. Quality assessment will be carried out using a checklist developed from the QUIPS and QUADAS-2 tools. Data will be grouped by tool and a narrative synthesis undertaken. For each tool, meta-analysis will be undertaken for ability to predict suicide or repeat self-harm where clinical and methodological homogeneity exists.

DISCUSSION

This systematic review will be the first to explore the use of assessment scales/tools in an adolescent population and will help to inform current practice regarding scales/tools with higher predictive ability. There is currently no evidence specifically for this population and a clear need with a high prevalence of self-harm and suicide in adolescents. Additionally, this review will help guide future research into suicide and self-harm prediction and prevention.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42017058686.

Genomic prediction using an iterative conditional expectation algorithm for a fast BayesC-like model

Genomic prediction is feasible for estimating genomic breeding values because of dense genome-wide markers and credible statistical methods, such as Genomic Best Linear Unbiased Prediction (GBLUP) and various Bayesian methods. Compared with GBLUP, Bayesian methods propose more flexible assumptions for the distributions of SNP effects. However, most Bayesian methods are performed based on Markov chain Monte Carlo (MCMC) algorithms, leading to computational efficiency challenges. Hence, some fast Bayesian approaches, such as fast BayesB (fBayesB), were proposed to speed up the calculation. This study proposed another fast Bayesian method termed fast BayesC (fBayesC). The prior distribution of fBayesC assumes that a SNP with probability γ has a non-zero effect which comes from a normal density with a common variance. The simulated data from QTLMAS XII workshop and actual data on large yellow croaker were used to compare the predictive results of fBayesB, fBayesC and (MCMC-based) BayesC. The results showed that when γ was set as a small value, such as 0.01 in the simulated data or 0.001 in the actual data, fBayesB and fBayesC yielded lower prediction accuracies (abilities) than BayesC. In the actual data, fBayesC could yield very similar predictive abilities as BayesC when γ ≥ 0.01. When γ = 0.01, fBayesB could also yield similar results as fBayesC and BayesC. However, fBayesB could not yield an explicit result when γ ≥ 0.1, but a similar situation was not observed for fBayesC. Moreover, the computational speed of fBayesC was significantly faster than that of BayesC, making fBayesC a promising method for genomic prediction.

Genomic relationships reveal significant dominance effects for growth in hybrid Eucalyptus

Non-additive genetic effects can be effectively exploited in control-pollinated families with the availability of genome-wide markers. We used 41,304 SNP markers and compared pedigree vs. marker-based genetic models by analysing height, diameter, basic density and pulp yield for Eucalyptus urophylla × E.grandis control-pollinated families represented by 949 informative individuals. We evaluated models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance). We showed that the models can capture a large proportion of the genetic variance from dominance and epistasis for growth traits as those components are typically not independent. We also showed that we could partition genetic variances more precisely when using relationship matrices derived from markers compared to using only pedigree information. In addition, phenotypic prediction accuracies were only slightly increased by including dominance effects for growth traits since estimates of non-additive variances yielded rather high standard errors. This novel result improves our current understanding of the architecture of quantitative traits and recommends accounting for dominance variance when developing genomic selection strategies in hybrid Eucalyptus.

Comparison of the Simplified sWHI and the Standard CHS Frailty Phenotypes for Prediction of Mortality, Incident Falls, and Hip Fractures in Older Women

Background

We compared the simplified Women's Health Initiative (sWHI) and the standard Cardiovascular Health Study (CHS) frailty phenotypes in predicting falls, hip fracture, and death in older women.

Methods

Participants are from the WHI Clinical Trial. CHS frailty criteria included weight loss, exhaustion, weakness, slowness, and low physical activity. The sWHI frailty score used two items from the RAND-36 physical function and vitality subscales, one item from the WHI physical activity scale plus the CHS weight loss criteria. Specifically, level of physical function was the capacity to walk one block and scored as severe (2-points), moderate (1-point), or no limitation (0). Vitality was based on feeling tired most or all of the time (1-point) versus less often (0). Low physical activity was walking outside less than twice a week (1-point) versus more often (0). A total score of 3 resulted in a frailty classification, a score of 1 or 2 defined pre-frailty, and 0 indicated nonfrailty. Outcomes were modeled using Cox regression and Harrell C-statistics were used for comparisons.

Results

Approximately 5% of the participants were frail based on the CHS or sWHI phenotype. The sWHI frailty phenotype was associated with higher rates of mortality (hazard ratio [HR] = 2.36, p ≤ .001) and falls (HR = 1.45, p = .005). Comparable HRs in CHS-phenotype were 1.97 (p < .001) and 1.36 (p = .03), respectively. Neither phenotype predicted hip fracture. Harrell C-statistics revealed nonsignificant differences in HRs between the CHS and sWHI frailty phenotypes.

Conclusion

The sWHI phenotype, which is self-reported and brief, might be practical in settings with limited resources.

Longitudinal predictive validity of emotional intelligence on first year medical students perceived stress

BACKGROUND

Emotional intelligence has been shown to affect academic performance and perceived stress. But conflicting reports suggest that the relationship between academic performance and emotional intelligence may not be straightforward. Hence, this study explored the relationship between emotional intelligence, perceived stress and academic performance.

METHODS

First year medical students were invited to participate in this longitudinal study. At Time 1, before mid-semester examinations, they completed the questionnaires on Schutte's Emotional Intelligence Scale (SEIS) and Perceived Stress Scale (PSS) (n = 213). At Time 2, before pre university examinations, students again completed perceived stress scale questionnaire. (n = 138). Academic performance was reported using summative assessment at both T1 and T2. The relationship between academic performance, emotional intelligence and perceived stress was explored using regression analysis.

RESULTS

Neither PSS nor SEIS were related to academic performance. However, perceived stress was significantly predicted by SEIS both at T1 (r = 0.333, β = 0.149, p < 0.001) as well as T2 (r = 0.240, β = 0.116, p = 0.028). The results were cross-validated at student level both at T1 and at T2.

CONCLUSION

Medical students with higher trait emotional intelligence perceived lesser stress. Therefore, it might be prudent to train medical students to increase their emotional intelligence to promote their well-being.

Predictive ability of urinary biomarkers for outcome in children with acute kidney injury

BACKGROUND

Urinary neutrophil gelatinase-associated lipocalin (NGAL), N-acetyl-beta-D-glucosaminidase (NAG), and interleukin 18 (IL-18) were found to be useful for early detection of acute kidney injury (AKI). The objective of this study was to determine the predictive ability of biomarkers for mortality and variation in levels in relation to different stages of AKI, need for dialysis, etiologies, and with duration of hospital stay.

METHODS

Urinary NGAL, NAG, and IL-18 levels were measured in 50 children with AKI and 30 age- and gender-matched healthy controls. AKI was classified as per pediatric Risk, Injury, Failure, Loss, and End-stage (RIFLE) criteria.

RESULTS

Median NGAL, NAG, and IL-18 values were significantly increased in AKI patients compared with controls (p < 0.001), with significant increase among risk, injury, and failure stages. Nonsurvivors had significantly higher median levels of NGAL (p = 0.008) and NAG (p = 0.018) than survivors. NGAL had highest area under the curve (AUC) at 0.750 [confidence interval (CI) 0.580-0.920], followed by NAG at 0.724 (CI 0.541-0.907), with sensitivity and specificity of 75 % each; and IL-18 (AUC 0.688, CI 0.511-0.864), with sensitivity 62.5 % and specificity 70.8 %, for predicting mortality. Values were significantly higher in patients who required peritoneal dialysis (PD) than in those in whom it was not indicated. Levels were comparable among different etiologies. Only NGAL level was found to be a significant risk factor associated with longer duration of hospital stay.

CONCLUSIONS

Urinary NGAL and NAG had modest predictive ability for mortality. Children requiring dialysis had significantly raised levels, and the NGAL level had significant association with duration of hospital stay.

Room for depressed and exhausted mood as a risk predictor for all-cause and cardiovascular mortality beyond the contribution of the classical somatic risk factors in men

BACKGROUND AND AIMS

Depressed mood and exhaustion (DEEX) have gained attention as a risk predictor for cardiovascular disease (CVD). Studies to estimate its ranking in prediction models are sparse.

METHODS

The study included 3428 men aged 45-74 years who participated in one of three population-based MONICA/KORA Augsburg surveys conducted between 1984 and 1995. Within a follow-up time of 10 years (31,791 person-years), 557 cases of all-cause mortality and 269 fatal CVD events were observed. Adjusted Cox proportional hazards models were used to assess mortality risks for DEEX and five classical cardiovascular risk factors. The predictive ability was evaluated by the area under the receiver-operating characteristic curve, the integrated discrimination improvement statistics and the net classification improvement.

RESULTS

The (crude) absolute mortality risk for DEEX was 23.1 cases per 1000 person-years for all-cause and 11.2 for CVD mortality. The adjusted hazard ratios of 1.52 for all-cause and 1.52 for CVD mortality (p < 0.01) were higher than those for hypercholesterolemia and obesity, but lower than for hypertension, smoking and diabetes. The improvements in risk prediction from DEEX were comparable to those of hypercholesterolemia and obesity, but substantially lower than those of hypertension, smoking and diabetes. The adjusted population-attributable risk (PAR) for DEEX accounted for about 15% for all-cause and CVD mortality, which gives DEEX a middle ranking amongst the classical risk factors.

CONCLUSIONS

DEEX is a strong predictor of mortality risk, ranking in a medium position amongst classical somatic risk factors.

Genomic Selection Using Extreme Phenotypes and Pre-Selection of SNPs in Large Yellow Croaker (Larimichthys crocea)

Genomic selection (GS) is an effective method to improve predictive accuracies of genetic values. However, high cost in genotyping will limit the application of this technology in some species. Therefore, it is necessary to find some methods to reduce the genotyping costs in genomic selection. Large yellow croaker is one of the most commercially important marine fish species in southeast China and Eastern Asia. In this study, genotyping-by-sequencing was used to construct the libraries for the NGS sequencing and find 29,748 SNPs in the genome. Two traits, eviscerated weight (EW) and the ratio between eviscerated weight and whole body weight (REW), were chosen to study. Two strategies to reduce the costs were proposed as follows: selecting extreme phenotypes (EP) for genotyping in reference population or pre-selecting SNPs to construct low-density marker panels in candidates. Three methods of pre-selection of SNPs, i.e., pre-selecting SNPs by absolute effects (SE), by single marker analysis (SMA), and by fixed intervals of sequence number (EL), were studied. The results showed that using EP was a feasible method to save the genotyping costs in reference population. Heritability did not seem to have obvious influences on the predictive abilities estimated by EP. Using SMA was the most feasible method to save the genotyping costs in candidates. In addition, the combination of EP and SMA in genomic selection also showed good results, especially for trait of REW. We also described how to apply the new methods in genomic selection and compared the genotyping costs before and after using the new methods. Our study may not only offer a reference for aquatic genomic breeding but also offer a reference for genomic prediction in other species including livestock and plants, etc.

Comparative analysis of the GBLUP, emBayesB, and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea)

Background

The advances of sequencing technology accelerate the development of theory of molecular quantitative genetics such as QTL mapping, genome-wide association study and genomic selection. This paper was designed to study genomic selection in large yellow croaker breeding. The aims of this study were: (i) to estimate heritability values of traits in large yellow croaker; (ii) to assess feasibility of genomic selection in the traits of growth rate and meat quality; (iii) to compare predictive accuracies affected by different algorithms and training sizes, and to find what training sizes could reach ideal accuracies; (iv) to compare results of GWAS with genomic prediction, and to assess feasibility of pre-selection of significant SNPs in genomic selection. 500 individuals were tested in the trait of body weight and body length, while 176 were tested in the percentage of n-3 highly unsaturated fatty acids (n-3HUFA) in muscle. GBLUP and emBayesB were used to perform genomic prediction.

Results

Genotyping-By-Sequencing method was used to construct the libraries for the NGS sequencing and find ~30,000 SNPs. Heritability estimates were 0.604, 0.586 and 0.438 for trait of body weight, body length and n-3HUFA, respectively. The predictive abilities estimated by GBLUP showed higher than that by emBayesB in traits of body weight and body length. However, the result was just the opposite in n-3HUFA. According to fit the curve of predictive accuracy, we estimated that at least 1000 individuals in training set could reach an accuracy of 0.8 in body weight and body length. GBLUP, emBayesB and GWAS could not always find significant SNPs associated with phenotypes consistently. Significant SNPs were selected by emBayesB could obtain the largest proportions to explain total additive genetic variances.

Conclusions

This research showed that genomic selection was feasible in large yellow croaker breeding. We suggest doing a test before deciding to use which algorithm in specific trait in genomic prediction. We estimated required training sizes to reach ideal predictive accuracies and assessed feasibility of pre-selection of SNPs successfully. Because of high mortality rate of fish and high cost in genomic sequencing, genomic selection may be more suitable for applying on some traits which cannot be measured on candidates directly.

Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models

Background

We adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common SNP improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients.

Methods

Adapted Bayesian sequential threshold models in combination with LASSO were applied to consider the time-to-event and the censoring nature of data. We studied 822 NMIBC patients followed-up >10 years. The study outcomes were time-to-first-recurrence and time-to-progression. The predictive ability of the models including up to 171,304 SNP and/or 6 clinico-pathological prognosticators was evaluated using AUC-ROC and determination coefficient.

Results

Clinico-pathological prognosticators explained a larger proportion of the time-to-first-recurrence (3.1 %) and time-to-progression (5.4 %) phenotypic variances than SNPs (1 and 0.01 %, respectively). Adding SNPs to the clinico-pathological-parameters model slightly improved the prediction of time-to-first-recurrence (up to 4 %). The prediction of time-to-progression using both clinico-pathological prognosticators and SNP did not improve. Heritability (ĥ²) of both outcomes was <1 % in NMIBC.

Conclusions

We adapted a Bayesian statistical learning method to deal with a large number of parameters in prognostic studies. Common SNPs showed a limited role in predicting NMIBC outcomes yielding a very low heritability for both outcomes. We report for the first time a heritability estimate for a disease outcome. Our method can be extended to other disease models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2361-7) contains supplementary material, which is available to authorized users.

The global activity limitation indicator and self-rated health: two complementary predictors of mortality

Background

The purpose of this study is to compare the ability of the Global Activity Limitation Indicator (GALI) and self-rated health (SRH) to predict all-cause mortality in the general adult population.

Methods

We linked the 2001 Belgian Health Interview Survey with mortality and migration registers 2001–2010. The baseline sample included 8,583 individuals aged 15 years and older. Poisson regression models were used to estimate the effect of the GALI and SRH on mortality rate during follow-up. We investigated the impact of gender, age, education and follow-up period on the association between the GALI/SRH and mortality.

Results

The GALI and SRH were strong and complementary predictors of mortality in the Belgian adult population. Although the two global instruments shared some traits, they predicted mortality concurrently, with some indication of a somewhat stronger effect for SRH. We found neither significant differences between men and women, nor between education groups. The predictive effect of the GALI and SRH slightly decreased over time and the predictive effect of SRH slightly decreased with age.

Conclusions

Our findings suggest that the GALI and SRH are useful and complementary measures for assessing the health and functional status of adults in population surveys.

Subjective memory impairment: No suitable criteria for case-finding of dementia in primary care

INTRODUCTION

Subjective memory impairment (SMI) might be used for the case-finding of dementia. Present analyses aim to determine the diagnostic value and the predictive ability of SMI and related worries for the discrimination of patients screened positive or negative for dementia.

METHODS

The analyses are based on data derived from the ongoing German general practioner (GP)-based, randomized controlled trial DelpHi-MV. A total of 5106 patients (age ≥ 70, living at home) were first asked for SMI and related worries and then screened for dementia in 110 participating GP practices (November 2011 to August 2014; preliminary data) using the DemTect.

RESULTS

A total number of 2556 patients (50%) stated that they experience SMI and 892 patients (17%) screened positive for dementia. The sensitivity of SMI for the correct classification of positively screened patients was 54%, the positive predictive value (PPV) 19%. The specificity of SMI was 51%; the negative predictive value (NPV) 84%. Among 2480 patients with SMI, 45% reported SMI-related worries (sensitivity 52%; specificity 57%; PPV 22%; NPV 84%). Receiver operating characteristics analyses showed no statistically significant improvement in the area under the curves when using SMI or related worries as predictors (additional to age and sex) for the discrimination between positively and negatively screened patients.

DISCUSSION

The analyses showed that the risk of overlooking cognitive impairment in the subgroup of patients who state that they do not experience SMI would be unreasonable high. Thus, the results provide clear evidence that neither SMI nor related worries can be used as a valid criteria to decide whether an elderly primary care patient should be tested for dementia.

TRIALS REGISTRATION

ClinicalTrials.gov Identifier: NCT01401582.

In silico model for predicting soil organic carbon normalized sorption coefficient (K(OC)) of organic chemicals

As a kind of in silico method, the methodology of quantitative structure-activity relationship (QSAR) has been shown to be an efficient way to predict soil organic carbon normalized sorption coefficients (KOC) values. In the present study, a total of 824 logKOC values were used to develop and validate a QSAR model for predicting KOC values. The model statistics parameters, adjusted determination coefficient (R(2)adj) of 0.854, the root mean square error (RMSE) of 0.472, the leave-one-out cross-validation squared correlation coefficient (Q(2)LOO) of 0.850, the external validation coefficient Q(2)ext of 0.761 and the RMSEext of 0.558 were obtained, which indicate satisfactory goodness of fit, robustness and predictive ability. The squared Moriguchi octanol-water partition coefficient (MLOGP2) explained 66.5% of the logKOC variance. The applicability domain of the current model has been extended to emerging pollutants like polybrominated diphenyl ethers, perfluorochemicals and heterocyclic toxins. The developed model can be used to predict the compounds with various functional groups including C=C, -C≡C-, -OH, -O-, -CHO, C=O, -C=O(O), -COOH, -C6H5, -NO2, -NH2, -NH-, N-, -N-N-, -NH-C(O)-NH-, -O-C(O)-NH2, -C(O)-NH2, -X(F, Cl, Br, I), -S-, -SH, -S(O)2-, -OS(O)2-, -NH-S(O)2-, (SR)2PH(OR)2 and Si.