How biomarker patterns can be utilized to identify individuals with a high disease burden: a bioinformatics approach towards predictive, preventive, and personalized (3P) medicine

Genetically predicted the causal relationship between gut microbiota and infertility: bidirectional Mendelian randomization analysis in the framework of predictive, preventive, and personalized medicine

Objective

Several studies have reported the association between gut microbiota and infertility; however, the causal association between them remains unclear. This study aimed to explore the causal relationship between gut microbiota and infertility and evaluate how specific gut microbiota can support early monitoring and prevention of infertility in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

The gut microbiota GWAS data included 18,340 individuals. Female infertility (6481 cases and 68,969 controls) and male infertility data (680 cases and 72,799 controls) were obtained from the FinnGen consortium. The inverse variance weighting (IVW), MR-Egger, weighted median (WM), Cochran Q tests, MR-PRESSO, and leave-one-out were used as a supplement to Mendelian randomization (MR) results and sensitivity analysis.

Results

The results of MR analysis indicated a significant causal association between Eubacterium oxidoreducens (OR = 2.048, P = 0.008), Lactococcus (OR = 1.445, P = 0.042), Eubacterium ventriosum (OR = 0.436, P = 0.018), Eubacterium rectale (OR = 0.306, P = 0.002), and Ruminococcaceae NK4A214 (OR = 0.537, P = 0.045) and male infertility. Genetically predicted Eubacterium ventriosum (OR = 0.809, P = 0.018), Holdemania (OR = 0.836, P = 0.037), Lactococcus (OR = 0.867, P = 0.020), Ruminococcaceae NK4A214 (OR = 0.830, P < 0.050), Ruminococcus torques (OR = 0.739, P = 0.022), and Faecalibacterium (OR = 1.311, P = 0.007) were associated with female infertility. Sensitivity analysis did not detect heterogeneity and pleiotropy (P > 0.05).

Conclusions

Our results provided evidence for the causal relationship between some gut microbiota and male and female infertility. These findings might be valuable in providing personalized treatment options for preventing infertility and improving reproductive function by monitoring and regulating the gut microbiota of infertility patients in the context of PPPM. Moreover, detecting the abundance of microbiota in feces can support preventive and personalized strategies, which may benefit more infertility patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00332-6.

Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014

Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.

Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM

Background/aims

Timely detection and treatment of retinal detachment (RD) could effectively save vision and reduce the risk of progressing visual field defects. High myopia (HM) is known to be associated with an increased risk of RD. Evidently, it should be clearly discriminated the individuals with high or low risk of RD in patients with HM. By using multi-parametric analysis, risk assessment, and other techniques, it is crucial to create cutting-edge screening programs that may be utilized to improve population eye health and develop person-specific, cost-effective preventative, and targeted therapeutic measures. Therefore, we propose a novel, routine blood parameters-based prediction model as a screening program to help distinguish who should offer detailed ophthalmic examinations for RD diagnosis, prevent visual field defect progression, and provide personalized, serial monitoring in the context of predictive, preventive, and personalized medicine (PPPM/3 PM).

Methods

This population-based study included 20,870 subjects (HM = 19,284, HMRD = 1586) who underwent detailed routine blood tests and ophthalmic evaluations. HMRD cases and HM controls were matched using a nested case-control design. Then, the HMRD cases and HM controls were randomly assigned to the discovery cohort, validation cohort 1, and validation cohort 2 maintaining a 6:2:2 ratio, and other subjects were assigned to the HM validation cohort. Receiver operating characteristic curve analysis was performed to select feature indexes. Feature indexes were integrated into seven algorithm models, and an optimal model was selected based on the highest area under the curve (AUC) and accuracy.

Results

Six feature indexes were selected: lymphocyte, basophil, mean platelet volume, platelet distribution width, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio. Among the algorithm models, the algorithm of conditional probability (ACP) showed the best performance achieving an AUC of 0.79, a diagnostic accuracy of 0.72, a sensitivity of 0.71, and a specificity of 0.74 in the discovery cohort. A good performance of the ACP model was also observed in the validation cohort 1 (AUC = 0.81, accuracy = 0.72, sensitivity = 0.71, specificity = 0.73) and validation cohort 2 (AUC = 0.77, accuracy = 0.71, sensitivity = 0.70, specificity = 0.72). In addition, ACP model calibration was found to be good across three cohorts. In the HM validation cohort, the ACP model achieved a diagnostic accuracy of 0.81 for negative classification.

Conclusion

We have developed a routine blood parameters-based model with an ACP algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring and predicting the occurrence of RD in HM and can facilitate the prevention of HM progression to RD. According to the current study, routine blood measures are essential in patient risk classification, predictive diagnosis, and targeted therapy. Therefore, for high-risk RD persons, novel screening programs and prompt treatment plans are essential to enhance individual outcomes and healthcare offered to the community with HM.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00319-3.

Integrating oculomics with genomics reveals imaging biomarkers for preventive and personalized prediction of arterial aneurysms

Objective

Arterial aneurysms are life-threatening but usually asymptomatic before requiring hospitalization. Oculomics of retinal vascular features (RVFs) extracted from retinal fundus images can reflect systemic vascular properties and therefore were hypothesized to provide valuable information on detecting the risk of aneurysms. By integrating oculomics with genomics, this study aimed to (i) identify predictive RVFs as imaging biomarkers for aneurysms and (ii) evaluate the value of these RVFs in supporting early detection of aneurysms in the context of predictive, preventive and personalized medicine (PPPM).

Methods

This study involved 51,597 UK Biobank participants who had retinal images available to extract oculomics of RVFs. Phenome-wide association analyses (PheWASs) were conducted to identify RVFs associated with the genetic risks of the main types of aneurysms, including abdominal aortic aneurysm (AAA), thoracic aneurysm (TAA), intracranial aneurysm (ICA) and Marfan syndrome (MFS). An aneurysm-RVF model was then developed to predict future aneurysms. The performance of the model was assessed in both derivation and validation cohorts and was compared with other models employing clinical risk factors. An RVF risk score was derived from our aneurysm-RVF model to identify patients with an increased risk of aneurysms.

Results

PheWAS identified a total of 32 RVFs that were significantly associated with the genetic risks of aneurysms. Of these, the number of vessels in the optic disc ('ntreeA') was associated with both AAA (β = -0.36, P = 6.75e-10) and ICA (β = -0.11, P = 5.51e-06). In addition, the mean angles between each artery branch ('curveangle_mean_a') were commonly associated with 4 MFS genes (FBN1: β = -0.10, P = 1.63e-12; COL16A1: β = -0.07, P = 3.14e-09; LOC105373592: β = -0.06, P = 1.89e-05; C8orf81/LOC441376: β = 0.07, P = 1.02e-05). The developed aneurysm-RVF model showed good discrimination ability in predicting the risks of aneurysms. In the derivation cohort, the C-index of the aneurysm-RVF model was 0.809 [95% CI: 0.780-0.838], which was similar to the clinical risk model (0.806 [0.778-0.834]) but higher than the baseline model (0.739 [0.733-0.746]). Similar performance was observed in the validation cohort, with a C-index of 0.798 (0.727-0.869) for the aneurysm-RVF model, 0.795 (0.718-0.871) for the clinical risk model and 0.719 (0.620-0.816) for the baseline model. An aneurysm risk score was derived from the aneurysm-RVF model for each study participant. The individuals in the upper tertile of the aneurysm risk score had a significantly higher risk of aneurysm compared to those in the lower tertile (hazard ratio = 17.8 [6.5-48.8], P = 1.02e-05).

Conclusion

We identified a significant association between certain RVFs and the risk of aneurysms and revealed the impressive capability of using RVFs to predict the future risk of aneurysms by a PPPM approach. Our finds have great potential to support not only the predictive diagnosis of aneurysms but also a preventive and more personalized screening plan which may benefit both patients and the healthcare system.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-023-00315-7.

Oculomics for sarcopenia prediction: a machine learning approach toward predictive, preventive, and personalized medicine

Aims

Sarcopenia is characterized by a gradual loss of skeletal muscle mass and strength with increased adverse outcomes. Recently, large-scale epidemiological studies have demonstrated a relationship between several chronic disorders and ocular pathological conditions using an oculomics approach. We hypothesized that sarcopenia can be predicted through eye examinations, without invasive tests or radiologic evaluations in the context of predictive, preventive, and personalized medicine (PPPM/3PM).

Methods

We analyzed data from the Korean National Health and Nutrition Examination Survey (KNHANES). The training set (80%, randomly selected from 2008 to 2010) data were used to construct the machine learning models. Internal (20%, randomly selected from 2008 to 2010) and external (from the KNHANES 2011) validation sets were used to assess the ability to predict sarcopenia. We included 8092 participants in the final dataset. Machine learning models (XGBoost) were trained on ophthalmological examinations and demographic factors to detect sarcopenia.

Results

In the exploratory analysis, decreased levator function (odds ratio [OR], 1.41; P value <0.001), cataracts (OR, 1.31; P value = 0.013), and age-related macular degeneration (OR, 1.38; P value = 0.026) were associated with an increased risk of sarcopenia in men. In women, an increased risk of sarcopenia was associated with blepharoptosis (OR, 1.23; P value = 0.038) and cataracts (OR, 1.29; P value = 0.010). The XGBoost technique showed areas under the receiver operating characteristic curves (AUCs) of 0.746 and 0.762 in men and women, respectively. The external validation achieved AUCs of 0.751 and 0.785 for men and women, respectively. For practical and fast hands-on experience with the predictive model for practitioners who may be willing to test the whole idea of sarcopenia prediction based on oculomics data, we developed a simple web-based calculator application (https://knhanesoculomics.github.io/sarcopenia) to predict the risk of sarcopenia and facilitate screening, based on the model established in this study.

Conclusion

Sarcopenia is treatable before the vicious cycle of sarcopenia-related deterioration begins. Therefore, early identification of individuals at a high risk of sarcopenia is essential in the context of PPPM. Our oculomics-based approach provides an effective strategy for sarcopenia prediction. The proposed method shows promise in significantly increasing the number of patients diagnosed with sarcopenia, potentially facilitating earlier intervention. Through patient oculometric monitoring, various pathological factors related to sarcopenia can be simultaneously analyzed, and doctors can provide personalized medical services according to each cause. Further studies are needed to confirm whether such a prediction algorithm can be used in real-world clinical settings to improve the diagnosis of sarcopenia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00292-3.

Prognostic significance of pretreatment red blood cell distribution width in primary diffuse large B-cell lymphoma of the central nervous system for 3P medical approaches in multiple cohorts

Background/aims

Predicting the clinical outcomes of primary diffuse large B-cell lymphoma of the central nervous system (PCNS-DLBCL) to methotrexate-based combination immunochemotherapy treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). The red blood cell distribution width (RDW) has been reported to be associated with the clinical outcomes of multiple cancer. However, its prognostic role in PCNS-DLBCL is yet to be evaluated. Therefore, we aimed to effectively stratify PCNS-DLBCL patients with different prognosis in advance and early identify the patients who were appropriate to methotrexate-based combination immunochemotherapy based on the pretreatment level of RDW and a clinical prognostic model.

Methods

A prospective-retrospective, multi-cohort study was conducted from 2010 to 2020. We evaluated RDW in 179 patients (retrospective discovery cohorts of Huashan Center and Renji Center and prospective validation cohort of Cancer Center) with PCNS-DLBCL treated with methotrexate-based combination immunochemotherapy. A generalized additive model with locally estimated scatterplot smoothing was used to identify the relationship between pretreatment RDW levels and clinical outcomes. The high vs low risk of RDW combined with MSKCC score was determined by a minimal P-value approach. The clinical outcomes in different groups were then investigated.

Results

The pretreatment RDW showed a U-shaped relationship with the risk of overall survival (OS, P = 0.047). The low RDW (< 12.6) and high RDW (> 13.4) groups showed significantly worse OS (P < 0.05) and progression-free survival (PFS; P < 0.05) than the median group (13.4 > RDW > 12.6) in the discovery and validation cohort, respectively. RDW could predict the clinical outcomes successfully. In the discovery cohort, RDW achieved the area under the receiver operating characteristic curve (AUC) of 0.9206 in predicting the clinical outcomes, and the predictive value (AUC = 0.7177) of RDW was verified in the validation cohort. In addition, RDW combined with MSKCC predictive model can distinguish clinical outcomes with the AUC of 0.8348 for OS and 0.8125 for PFS. Compared with the RDW and MSKCC prognosis variables, the RDW combined with MSKCC scores better identified a subgroup of patients with favorable long-term survival in the validation cohort (P < 0.001). RDW combined MSKCC score remained to be independently associated with clinical outcomes by multivariable analysis.

Conclusions

Based on the pretreatment RDW and MSKCC scores, a novel predictive tool was established to stratify PCNS-DLBCL patients with different prognosis effectively. The predictive model developed accordingly is promising to judge the response of PCNS-DLBCL to methotrexate-based combination immunochemotherapy treatment. Thus, hematologists and oncologists could tailor and adjust therapeutic modalities by monitoring RDW in a prospective rather than the reactive manner, which could save medical expenditures and is a key concept in 3PM. In brief, RDW combined with MSKCC model could serve as an important tool for predicting the response to different treatment and the clinical outcomes for PCNS-DLBCL, which could conform with the principles of predictive, preventive, and personalized medicine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00290-5.

Biochemical clusters predict mortality and reported inability to work 10 ​years later

BACKGROUND

Chronic systemic inflammation has been linked to premature mortality and limited somatic as well as mental health with consequences for capability to work and everyday functioning. We recently identified three biochemical clusters of endocrine and immune parameters (C-reactive protein (CRP), interleukin-6 (IL-6), fibrinogen, cortisol and creatinine) in participants, age 35-81 years, of the open access Midlife in the United States Study (MIDUS) dataset. These clusters have been validated in an independent cohort of Japanese mid-life adults. Among these clusters, the one characterized by high inflammation coupled with low cortisol and creatinine concentrations was associated with the highest disease burden, referred to as high-risk cluster in the following. The current study aims to further examine the nature of this cluster and specifically whether it predicts mortality and the reported inability to work the last 30 days 10 years after the biomarker assessment.

METHODS AND FINDINGS

Longitudinally assessed health data from N ​= ​1234 individuals were analyzed in the current study. Logistic regression analyses were performed to predict mortality within one decade after first assessment (T0 ​= ​first assessment; T1 ​= ​second assessment). General linear models were used to predict the number of days study participants were unable to work due to health issues in the last 30 days (assessed at T1, analyses restricted to individuals <70 years of age). Biological sex, disease burden, and age at T0 were used as covariates in all analyses. Individuals in the previously identified high-risk cluster had a higher risk for mortality (22% of individuals deceased between T0 and T1 versus 10% respectively 9% in the two other clusters). Logistic regression models predicting mortality resulted in a significant difference between individuals from the high-risk cluster compared to those from an identified reference cluster (indicator method, p ​= ​.012), independently of age and disease burden. Furthermore, individuals in the high-risk cluster reported a higher number of disability days during the past 30 days (3.4 days in the high-risk cluster versus 1.5 respectively 1.0 days in the reference clusters) assessed at T1. All pairwise comparisons involving the high-risk cluster were significant (all ps ​< ​.001).

CONCLUSIONS

Immune-endocrine profiles are predictive of mortality within the following decade over and above age and disease burden. The findings thus highlight the importance of biomarker-based risk profiling that may provide new targets for interventions in the context of preventive medicine in the transition from health to disease and disease-related mortality.

Effects of Breast-Conserving Surgery Combined with Immediate Autologous Fat Grafting on Oncologic Safety, Satisfaction and Psychology in Patients with Breast Cancer: A Retrospective Cohort Study

Purpose

Autologous fat grafting (AFG) is a technique that can improve the appearance of breasts in surgical patients. There are currently few studies on breast-conserving surgery (BCS) combined with immediate AFG, although we believe that it could achieve satisfactory effects. Therefore, the purpose of this study is to observe the effects of BCS combined with immediate AFG on oncologic safety, satisfaction and psychology of breast cancer patients.

Patients and Methods

We retrospectively collected the data of 85 breast cancer patients from February 2018 to October 2018. After screening, 40 patients in AFG group (AG, BCS combined with immediate AFG) and 40 patients in control group (CG, BCS alone) were finally included in the study. The primary outcomes were the survival, tumor recurrence and metastasis, and BREAST-Q score of patients. The secondary outcomes were short and long-term complications, degree of depression and anxiety of patients.

Results

A total of 80 patients were included in the analysis. There was no significant difference in the clinicopathological data between the two groups (P>0.05). The average follow-up time of the two groups was 40.58±2.630 and 40.28±2.679 months. In the analysis of oncologic safety, no patients died in AG and 1 patient died in CG. In addition, there was no significant difference between the two groups in terms of the overall recurrence rate and the distribution of recurrence types (P>0.05). As for satisfaction, the BREAST-Q score of AG was significantly higher than that of CG (57.85±4.833 vs 51.93±5.045, P<0.001). In the secondary outcomes, there was no short-term complication specified in the study; in the long-term complications, the incidence of calcification in AG was not significantly higher than that in CG (P=0.065). In the analysis of depression and anxiety, there was no significant difference between the two groups (P>0.05).

Conclusion

BCS combined with immediate AFG can significantly improve patients’ satisfaction without increasing the risk of death and tumor recurrence. However, it does not seem to play a role in improving the conditions of depression and anxiety.

Association of systemic inflammation indices with visual field loss progression in patients with primary angle-closure glaucoma: potential biomarkers for 3P medical approaches

RELEVANCE

Accumulating evidence suggests a dysfunction of the para-inflammation in the retinal ganglion cell layer and the optic nerve head in patients with glaucoma. Currently, circulating blood platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-monocyte ratio (LMR) are regarded as novel indicators of systemic inflammation. Biomarkers allow early identification of patients with visual field (VF) loss progression and timely implementation of replacement therapies.

OBJECTIVE

This study aimed to investigate whether higher inflammatory indices (PLR, NLR, and LMR) were associated with VF loss progression in patients with primary angle-closure glaucoma (PACG) for the predictive diagnostics, targeted prevention, and personalization of medical services.

METHODS

This prospective cohort study followed up 277 patients with PACG for at least 24 months, with clinical examination and VF testing every 6 months. Inflammatory cell quantification, including platelets, neutrophils, lymphocytes, and monocytes, was measured using the Sysmex XN-A1 automated inflammatory cells quantification system. Three systemic inflammatory indices, PLR, NLR, and LMR, were determined on the basis of baseline neutrophil, lymphocyte, monocyte, and platelet counts in patients with PACG. The risk factors for PACG were analyzed using logistic regression, Cox proportional hazards regression, and the Kaplan-Meier curve.

RESULTS

Our results revealed that 111 (40.07%) patients showed VF loss progression. The PLR was significantly higher (P = 0.046) in the progression group than in the non-progression group. A higher PLR (OR 1.05, 95% CI 1.01-1.08, P = 0.004) was a risk factor for PACG progression. In multivariate analyses, PLR independently predicted VF loss progression (HR 1.01, 95% CI 1.00-1.01, P = 0.04). Kaplan-Meier curve analysis showed that higher PLR indicated significantly higher rates of VF loss progression (66.91% vs. 52.90%, P = 0.03). Comparable results were observed in the male and female subgroups.

CONCLUSION

Our findings revealed the significant association between a high PLR and a greater risk of VF loss progression in patients with PACG. PLR may be highly recommended as a novel predictive/diagnostic tool for the assessment of VF loss progression from the perspectives of predictive, preventive, and personalized medicine in vulnerable populations and for individual screening.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-021-00260-3.