This model implies that a temporary disturbance of neuronal networks (disruption) is followed by a trophic effect (potentiation), which enables the rewiring of neuronal circuits to a more euthymic functioning brain. We hypothesized that disruption of neuronal networks could trigger biochemical alterations leading to a temporary decrease in N-acetylaspartate , while choline (a membrane component), myo-Inositol , and glutamate/glutamine  were postulated to increase. Previous magnetic resonance spectroscopy studies, reporting diverse findings, have used two different referencing methods - creatine ratios and tissue corrected values referenced to water – for the quantification of brain metabolites. Changes in creatine during ECT have also been reported, which may confound estimates adopting this as an internal reference.Based on previous research on electroconvulsive therapy (ECT) we have proposed a model where disrupt, potentiate, and rewire hypothesis. We used both creatine ratios and water-scaled values for the quantification of brain metabolites to validate the results across referencing methods.Using MR spectroscopy, we investigated 31 moderately to severely depressed patients and 19 healthy controls before, during, and after ECT or at similar time points (for controls). We tested whether biochemical alterations in tNAA, choline, mI, and Glx lend support to the p = 0.007, creatine ratio) and 3%  but returned to baseline 6 months after ECT. Compared to after treatment series tNAA levels at 6-month follow-up had increased in both creatine ratio  and water referenced data . Findings for other brain metabolites varied and could not be validated across referencing methods.Levels of tNAA in the anterior cingulate cortex decreased after an ECT treatment series (average 10.6 sessions) by 6% (disrupt, potentiate, and rewire model.Our findings suggest that prior research must be interpreted with care, as several referencing and processing methods have been used in the past. Yet, the results for tNAA were robust across quantification methods and concur with relevant parts of the  Electroconvulsive therapy (ECT) is a therapy for depression that is mainly used in non-responders to antidepressant pharmacotherapy and in patients requiring fast and effective symptom alleviation . The trein vivo. By exploiting the differences in resonance frequency between molecules certain metabolites can be studied. This gives a unique opportunity to study the neurobiological underpinnings of ECT. Most commonly the hydrogen nucleus (a single proton) is used as origin for the MRS signal, giving the 1H-MRS spectrum. The total received signal, hence the estimated amplitude and area under the curve, will depend on several factors, including field strength, relaxation effects, and coil properties and loading; several of these are difficult to reliably control for. As such, a stable reference signal is commonly adopted to scale the amplitude and correct for these unknown factors. Though processing pipelines vary, two main referencing methods are used: the metabolite ratio relative to total creatine (/tCr), or water referenced values (/H2O). Both have been used in previous ECT research (MR spectroscopy (MRS) is a practical and non-invasive MR-technique that allows investigation of the brain’s neurobiology research . Since vresearch  we have The disrupt, potentiate, and rewire (DPR) hypothesis  suggests1H-MRS spectrum of the healthy brain. Decreased levels of NAA are seen in brain injury and disease, and in 1H-MRS, NAA is considered a marker of neuronal integrity  are comprised of NAA and closely resonating NAAG, which only amounts to a small part of the signal intensity , phosphocholine (PCh), and free choline, often reported together as total choline (tCho). An increase in choline may reflect both choline synthesis and membrane damage, hence it must be interpreted with care , and as an osmolyte . The antd levels , which id levels . Additiod levels , and a md levels  suggesteferation , and is feration , though feration . In lighferation .1H-MRS at 3T it is difficult to distinguish from Glutamine (Gln), hence the two are often reported together as Glx – wherein Glu is usually the dominant component due to its substantially higher concentration. Its concentration has been measured to be lower in depressed subjects compared to healthy controls (Glutamate (Glu) is the main excitatory neurotransmitter in the brain; measured with controls , 14. Botcontrols . Howevercontrols . Within 1H-MRS originates from Creatine (Cr) and phosphocreatine (PCr). tCr is often assumed to be somewhat stable and is therefore often chosen as an internal concentration reference. However, Cr concentration in the brain may be related to neural activity and/or vascularization and has been shown to vary with intake  signal in h intake  and in ch intake , therefoh intake , 31. Oned to ECT . Both crIn this investigation, we aimed to investigate metabolite changes during ECT treatment and relate them to the DPR hypothesis. Specifically, we hypothesized that tNAA levels decrease after ECT treatment, due to temporary disruption of neuronal integrity. At 6-month follow-up, tNAA levels return to baseline levels or higher. There is a negative correlation between tNAA and everyday memory impairment. tCho levels increase after ECT due to temporary disruption of cell membranes. This increase from baseline is no longer seen at 6-month follow-up. There is a positive correlation between tCho rise and everyday memory impairment. Baseline mI levels are lower in patients compared to controls and increase during treatment. After the ECT series, and at follow-up, mI levels remain increased for responders indicating potentiation and rewiring. Glx levels at baseline are lower in patients compared to controls. Glx levels increase with ECT and remain increased at 6-month follow-up but have their peak levels after ECT treatment series, possibly as a part of the mechanism behind disruption.This study was approved by the Regional Committee for Medical and Health Research Ethics, REC South East, Norway 2013/1032). The protocol of this study has been published previously  (EMQ-28) , 35 is aRight unilateral (RUL) electrode placement ECT was performed using a Thymatron System IV . The initial stimulus charge was calculated by an age-based algorithm, where the patient’s age in years x5 ≅ stimulus charge in mC. The stimulus was increased during the treatment series due to increase in seizure threshold. All patients were administered anesthesia  and neuromuscular blockade (succinylcholine). All patients were hyperoxygenated before and during anesthesia.Patients were scanned at four timepoints: 1–2 h before treatment (Baseline), 1–2 h after first treatment (TP2), 7–14 days after completion of ECT series (TP3), and 6 months after ECT series (TP4). HC were scanned at similar time intervals as patients. The study flowchart can be seen in 1H-MRS-spectrum for patients. Metabolite concentrations are reported as mean ± SD.Imaging and MRS were performed on a 3 Tesla GE Discovery 750 scanner system . A 32-channel head coil was used. For voxel localization, a 3D T1 weighted fast spoiled gradient echo (FSPGR) sequence was used . The single voxel point resolved spectroscopy (SV-PRESS) voxel was placed in the anterior cingulate cortex (ACC), angled to follow the foremost slope of the corpus callosum . Voxel pt-tests  or Pearson chi squared tests (for sex).Statistical analyses were performed using the R statistical software, version 4.1.3 . All ana1H-MRS data the nlme package  = 0.66, p = 0.29, two samples t-test) and sex  = 1, p = 1 Pearson chi squared test). Healthy controls displayed no consistent changes in metabolite concentrations across referencing methods  at baseline, 24 patients (16 HC) at TP2, 24 patients (16 HC) at TP 3 and 21 patients (17 HC) at TP4. Because of incorrect segmentation, data from 1 patient was removed after visual inspection. For two patients, a new treatment series was required within 6 months and the follow-up was rescheduled 6 months after start of the second ECT series. For HC, 11 of 19 participants were female, age ranged from 21–69 years . Patient characteristics are given in t(24) = 8.94, p < 0.001, d = 1.79] in all but two patients and remained lower compared to baseline at 6-month follow-up in 20 out of 21 patients (TP4)  computed by paired samples t-tests. EMQ-28 scores did not differ from baseline 119 (35) to after ECT-series 111 (29) or to 6-month follow-up 115 (37).Patients’ MADRS scores decreased from baseline [34.1 (5.2)] to after ECT-series (TP3)  and the signal to noise ratio (SNR) was 22.84–65.53 . The creatine ratio and water referenced metabolite concentrations for patients and controls at baseline and the percentage change to TP3 for patients are listed in t-test t(40) = −2.85, p = 0.007, d = −0.90. Likewise, tNAA/H2O levels were lower in patients 16.52 IU (0.94) compared to HC 16.70 IU (0.78), but this was not significant: t(40) = −0.64, p = 0.52. Longitudinal changes in patients were investigated with a mixed effects model, where timepoint affected both tNAA/tCr levels  and tNAA/H2O levels  in patients at TP3, resulting in a decrease compared to baseline levels. There were no significant changes in tNAA/H2O or tNAA/tCr from baseline to any other timepoint. When comparing TP3 to TP4 in the same model tNAA/tCr-levels increased [t(60) = −4.25 p < 0.001], for tNAA/H2O: . See tNAA/tCr levels at baseline differed between patients 1.41 IU (0.12) and HC 1.51 IU (0.11) computed by a two samples 2O levels predicting MADRS or EMQ-28 were not significant for TP1, TP3 or the change between the two. tNAA/tCr and tNAA/H2O levels at TP3 did not differ between patients who had the voxel on the left versus on the right side compared by a two samples t-test.Linear models for tNAA/tCr and tNAA/H2O was significantly lower in controls [2.84 IU (0.27)] compared to patients [3.17 IU (0.47)], t(40) = 2.56, p = 0.01, but this was not seen for tCho/Cre. There were no significant changes in tCho/tCr or tCho/H2O at any timepoint when compared to baseline, investigated with linear mixed effects models. There were no significant changes in tCho/H2O or tCho/tCr from baseline to any other timepoint, tested with a linear mixed effects model. Tested with a linear model there was no association between tCho/tCr or tCho/H2O and EMQ-28 at baseline or when comparing the change from before to after treatment.tCho/H2O levels were higher in patients  compared to controls  at baseline, but findings were only significant for mI/H2O . There were no significant changes in mI/H2O or mI/tCr from baseline to any other timepoint, tested with a linear mixed effects model. Linear models for mI/tCr and mI/H2O levels predicting MADRS were not significant for TP1, TP3 or the change between the two.Both mI/tCr and mI/H2O levels [16.18 IU (1.60)] than controls [14.66 IU (11.79)], t(40) = 2.88, p = 0.006. The same trend was found for Glx/tCr levels: patients [1.29 IU (0.16)], controls [1.24 IU (0.18)], however this was not significant using a two samples t-test . A linear mixed effects model showed no impact of timepoint on Glx/tCr or Glx/H2O.Patients had higher baseline Glx/H1H-MRS study, we investigated creatine ratios and water referenced estimates of tNAA, tCho, mI, and Glx in 31 patients receiving ECT and 19 healthy controls. Patients were scanned at baseline, after the first ECT, after the ECT treatment series, and at six-month follow-up. HC, not receiving ECT, were scanned at similar time points. Our findings showed that ECT causes a reversible decrease in tNAA. Though the direction of the change in metabolite concentration mainly was the same across referencing methods  neither tCho, mI, or Glx displayed a significant change across referencing methods. No changes were found in any of the metabolites at the timepoint 2 h after the first ECT treatment. Possible explanations for this could be that: one single ECT treatment is not sufficient to induce metabolite changes on a level detectable with MRS or delay in detectable metabolite turnover. In the following paragraphs, we interpret our findings according to the DPR-hypothesis.In this We hypothesized that MRS correlates of disruption could be seen as a decrease in tNAA , an increase in tCho (membrane component), and an increase in Glx . We found a significant decrease in the patients’ tNAA levels from baseline to after the ECT series, in both creatine ratio and water referenced data. This finding is concordant with findings from several previous studies , 43–47.Choline is present in cell membranes and has previously been reported to increase after ECT . An incrdisruption is reversible over time.An increase in mI  was hypothesized to coincide with the alleviation of depressive symptoms and would support potentiation (increased plasticity) and rewiring. Similarly, an increase in NAA or a return to baseline after the first decrease (disruption), could also be interpreted as either end of the disruptive effect or rewiring through axonal recovery after neuronal disturbances, as reviewed by Burtscher and Holtås where such a mechanism was suggested for epilepsy . PreviouLinear models for tNAA levels predicting MADRS were not significant, suggesting that the disruptive effects of the treatment could not alone explain the effect of the ECT on depressive symptoms. Linear models for mI levels predicting MADRS were not significant for TP1, TP3, or the change between the two. Other hypotheses, though not ECT-specific, have argued that both increased and reduced levels of mI might play a role in the alleviation of depressive symptoms. However, a sample studied by Njau et al.  could noWe also hypothesized a correlation between subjective memory complaints and both tNAA-levels  and tCho-levels (increased memory complaints due to disruption of cell membranes: higher levels of tCho). Linear models for tNAA levels predicting EMQ-28 were not significant, and we found no association between tCho and EMQ-28 at baseline or when comparing the change from before to after treatment. EMQ-28 is a measure of subjective everyday memory, and the correlation to depression severity seems uncertain, as both correlation and no correlation have been found , 53. AltOur study is based on results from 31 patients and 19 healthy controls. A larger sample size would increase statistical power and possibly allow the quantification of more subtle changes in metabolite concentrations. A larger sample size would also allow for subgroup analysis based on patient heterogeneity . Such analyses may explain results that are now interpreted as conflicting. Previous investigations have mainly been of smaller samples. Consortia like the global ECT-MRI research collaboration  may givIt can be reasoned that psychotic or elderly patients who have the largest effect of ECT , 56 alsop-value, but are only one model. Our investigation was hypothesis-driven, and two referencing methods were used, strengthening results that are significant across referencing methods.During the statistical analysis, correction for multiple testing was not performed. The significance of the results must therefore be interpreted accordingly. Correction for multiple testing has some weaknesses as it can reduce statistical power and introduce type II errors. Hence, true differences may remain undiscovered. Additionally, the number of tests performed may be difficult to establish, as complex models yield several Due to the large voxel localized in the ACC region, a heterogeneous tissue composition including both gray and white matter are measured. Although Osprey  was usedIn previous MRS investigations of ECT, findings have been inhomogeneous. This could in part be due to differences in the choice of referencing method. Hence, we have used both water referenced results as well as the creatine ratio for the quantification of brain metabolites in our sample. Still, this approach does not reflect the whole variety of different MRS post processing pipelines that have been used in previous research. A more methodological comparison between methods is out of the scope of this article.Using both creatine ratio and water reference for MRS-data quantification, our study indicates a decrease in tNAA levels after ECT. This was reversed to pre-ECT levels 6 months after ECT. This finding lends support to temporary disruption as suggested in the “disrupt, potentiate, and rewire” hypothesis. Longitudinal changes in mI, tCho, or Glx levels were not consistent across quantification method, and we did not find any correlation between tNAA or tCho and effect or side effects of ECT. For future research, MR-spectroscopy investigations with voxel placement in areas that have an even stronger implication in the setting of depression, and which are more affected by ECT, such as the hippocampus and amygdala, may further shed light on the disrupt, potentiate, and rewire hypothesis. Other methods, such as Diffusion Tensor Imaging and resting state functional MRI may be better to assess neuronal potentiation and rewiring. Furthermore, larger sample sizes and multi-site investigations are important to improve our understanding of brain metabolites and their role in the neurobiological underpinnings of ECT.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by The Regional Committee for Medical and Health Research Ethics, REC South East, Norway (2013/1032). The patients/participants provided their written informed consent to participate in this study.VE: formal analysis, writing original draft, and writing-review and editing. LO and LE: conceptualization, supervision, methodology, and writing-review and editing. UK: conceptualization, methodology, and writing-review and editing. KO: conceptualization, supervision, and writing-review and editing. AC: software, formal analysis, and writing-review and editing. JH: conceptualization and writing-review and editing. FR: methodology and writing-review and editing. CB-J: methodology, statistical analysis, and writing-review and editing. All authors contributed to the article and approved the submitted version.Funding was received from the Western Norway Regional Health Authority (KO: no. 911986 and LO: no. 912238) and the University of Bergen (LO). The granting agencies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of data; or preparation, review, or approval of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


There is mounting evidence that the Mediterranean diet prevents type 2 diabetes, but little is known about the role of Mediterranean lifestyles other than diet and among non-Mediterranean populations. This work aimed to examine the association between a comprehensive Mediterranean-type lifestyle and type 2 diabetes incidence in a British adult population.We used data from 112,493 individuals free of cardiovascular disease and type 2 diabetes mellitus, aged 40–69 years, from the UK Biobank cohort, who were followed from 2009 to 2010 to 2021. The Mediterranean lifestyle was assessed through the 25-item MEDLIFE index, which comprises three blocks: (a) “Mediterranean food consumption”, (b) “Mediterranean dietary habits”, (c) “Physical activity, rest, social habits, and conviviality”. Diabetes incidence was obtained from clinical records. Cox proportional-hazards regression models were used to analyze associations and adjusted for the main potential confounders.p-trend < 0.001). All three blocks of MEDLIFE were independently associated with lower risk of diabetes.After a median follow-up of 9.4 years, 2,724 cases of type 2 diabetes were ascertained. Compared to the first quartile of MEDLIFE adherence, the hazard ratios  for increasing quartiles of adherence were 0.90 (0.82–0.99), 0.80 (0.72–0.89) and 0.70 (0.62–0.79) (Higher adherence to the MEDLIFE index was associated with lower risk of type 2 diabetes in the UK Biobank. A Mediterranean-type lifestyle, culturally adapted to non-Mediterranean populations, could help prevent diabetes.The online version contains supplementary material available at 10.1186/s12933-023-01999-x. Type 2 diabetes mellitus is a metabolic disease, a cardiovascular risk factor and a major cause of disability and death . With ansiesta”), adequate night sleep, doing sufficient physical activity and high levels of socialization. Systematic reviews of prospective studies have reported a linear [The Mediterranean lifestyle is a set of behaviors from the people living in the Mediterranean basin, characterized by a plant-based diet with little animal food consumption mainly in form of dairy, fish and white meat, with olive oil as the main fat source, along with other behaviors such us taking short naps  with around 500,000 participants aged 40–69 years at the time of enrollment. Study methods have been reported elsewhere , 16. BriDiet at baseline (2009–2012) was assessed via web-based 24-hour dietary assessment (Oxford WebQ)  up to fiThe MEDLIFE index consists of three blocks : (a) “Mesofrito , for nibbling outside meals, and eating in company, since the UK Biobank did not collect such information.To calculate the MEDLIFE index in the UK Biobank, we had to adjust some items according to previous studies , 21. ThePrevalent diabetes status  and date of diagnosis at baseline were obtained from self-reports and linkage of clinical records. For incident type 2 diabetes, diagnosis was obtained from self-report, death records, hospital inpatient records and primary care records. Hospital inpatient data were used up to 30th September 2021 for participants in England, up to 31st July 2021 for Scotland, and 31st March 2016 for Wales; primary care data were used up to 31st May 2016 or 31st May 2017 (depending on the data provider) for participants in England, 31st March 2017 for Scotland, and 31st August 2017 for Wales. Participants who died were assigned date of death as a censoring date. Dates of type 2 diabetes incidence correspond to the first reported diagnosis. Cases of type 2 diabetes mellitus were identified with ICD10 code E11.The following potential confounding factors were chosen based on previous literature on the field. We used self-reported information at baseline on sex, age, ethnicity (white or non-white), educational level (university or not university education), socio-economic status (Townsend deprivation index) and smoking status. Body mass index (BMI) was calculated as weight (kg) divided by squared height (m), both measured at baseline. Prevalent hypertension was defined as use of hypertensive medication or hypertension diagnosis by self-report and linkage of clinical records (confirmed using ICD10 codification). Use of cholesterol-lowering medication was self-reported. Prevalent cardiovascular disease, a criterion of exclusion, was defined as having suffered heart attack, angina, or stroke, by self-report or linkage from medical records. Family history of diabetes was assessed by self-report at baseline and follow-up.n = 106) to avoid exposure misclassification, together with those lacking sociodemographic data (n = 877), smoking status (n = 220), BMI (n = 274) cholesterol medication (n = 4), information on chronic disease status  and family history of diabetes . Participants with prevalent diabetes  or cardiovascular disease  were also excluded from the analyses. Therefore, the analytical sample included 112,493 participants  and family history of diabetes. We replicated the analyses first for each MEDLIFE block adjusting for the remaining blocks, and second for MEDLIFE individual items, adjusting for the MEDLIFE index excluding the corresponding item. We evaluated deviation from linearity with a 3-knots restricted cubic spline Cox regression model, adjusted as in Model 3. Knots were set at percentiles 10, 50 and 90 of the MEDLIFE distribution . Proportional hazards assumption was assessed plotting the survival probability of diabetes over follow-up for the MEDLIFE quartiles; the graphical representation provided no apparent evidence of violation of the assumption.To assess the association of MEDLIFE with incident type 2 diabetes, we used multivariable Cox models, using age as the underlying timescale, to estimate hazard ratios (HR) and 95% confidence intervals (CI). The MEDLIFE index (exposure) was modelled in quartiles and per 2 points increments, and the first quartile (lowest adherence) was used as reference. N = 116,832); 2) only in those having at least three diet assessments  to assess possible deviations from usual diet; 3) excluding wine from the MEDLIFE score and adjusting for alcohol consumption to assess the independent association of the MEDLFE with diabetes independently of wine, a characteristic component of the MD with controversial effects on health (24 items); 4) excluding the cases diagnosed in the first two years of follow-up to account for reverse causality ; 5) using the Fine-Gray model for competing mortality risks [In sensitivity analyses, we replicated the main analysis: 1) including participants with cardiovascular disease . Statistical significance was set at two-sided At baseline, participants had a mean (SD) age of 58.7 years and 57.7% were women. Mean (SD) MEDLIFE score was 9.57 (2.60). Participants with lower MEDLIFE score were more likely to be men, smokers, and hypertensives, and to have lower educational level and higher BMI Table . Comparep-trend < 0.001). Per each 2 points increase in MEDLIFE, the HR was 0.90 (0.87–0.92) (p < 0.001) , 2,724 incident cases of type 2 diabetes were ascertained (Table p = 0.001) for Block 1 (“Mediterranean food consumption”), 0.86 (0.81–0.92) (p < 0.001) for Block 2 (“Mediterranean dietary habits”), and 0.88 (0.82–0.93) (p < 0.001) for Block 3 , and excluding each item from MEDLIFE . In stratified analyses, the association remained in all strata except in participants with a BMI.In this cohort of middle-aged and older adults from the UK, the MEDLIFE score was associated in a dose-response manner with lower risk of type 2 diabetes. Additionally, the three blocks of MEDLIFE were independently associated with lower type 2 diabetes risk.sofrito, which are characteristic components of the Mediterranean diet, has not precluded observing a beneficial association between a Mediterranean-like lifestyle and diabetes. Notwithstanding this, not considering olive oil may have underestimated the effect of the Mediterranean lifestyle because of the well-known inverse association between olive oil consumption and diabetes [Our findings remark the importance of not only following a healthy diet or doing physical activity but adhering to an overall healthful lifestyle. Also, this work highlights the possibility to transpose and adapt a Mediterranean-type lifestyle to non-Mediterranean countries; indeed, the fact that in this analysis the MEDLIFE did not include olive oil or diabetes . LikewisIn our study we observed a 30% lower risk of type 2 diabetes for the highest versus lowest quartile of MEDLIFE. These results are in line with those of a meta-analysis of studies assessing different lifestyles in relation to type 2 diabetes . For insIn this analysis, we have found that the three blocks of MEDLIFE were independently associated with a lower risk of type 2 diabetes. The Block 1 (“Mediterranean food consumption”) represents the MD, and its protective association with diabetes is strongly supported by multiple reviews and meta-analyses of the literature , 7. ManyRegarding Block 2 (“Mediterranean dietary habits”), we had previously found an independent association with the other health outcomes (i.e. metabolic syndrome)  in a preFinally, Block 3  was also strongly associated with lower risk of type 2 diabetes, which concurs with previous studies , 34, 42.In our analyses, we found an unexpected direct association between napping and diabetes. In prior MEDLIFE studies, we found that short naps were inversely associated with all-cause mortality  and CVD Our findings were robust in several sensitivity analyses. Of note is the significant interaction observed between the MEDLIFE and BMI. The stratified analyses showed significant association for overweight and obese participants only, which showed most of the cases. In our study, the HR (per 2 points increases) went from 0.81 for Model 2 to 0.89 for Model 3 (where BMI and cardiovascular risk factors were included). After excluding BMI from Model 3, the HR went down from 0.90 to 0.83 (0.80–0.86) (data not shown). It is likely that BMI partly mediates the MEDLIFE-type 2 diabetes association, because excess of adiposity is the single most important determinant . Indeed,The MEDLIFE index was designed to include different lifestyle behaviors − with interrelated biological mechanisms − characterizing the Mediterranean culture and way of living and that may have synergistic effects. Some underlying biological mechanisms of the effect of the MD on type 2 diabetes include: (1) the antioxidant and anti-inflammatory properties of this diet pattern  that improves HbA1c levels, fasting glucose homeostasis, insulin levels and insulin resistance index (HOMA) . (2) HigPhysical activity have many benefits on cardiometabolic health: it maintains arterial compliance and function, reduces systemic inflammation biomarkers and reactive oxygen species, enhances antioxidant proteins, reduces mitochondrial fission and improves insulin sensitivity . Poor reStrengths of this study include the large number of participants, its prospective design, the long follow-up, the use of a score (MEDLIFE) to assess lifestyle which was previously validated in a Mediterranean population  and has In this cohort of middle-age and older adults from the United Kingdom, higher adherence to the MEDLIFE index was associated with lower risk of type 2 diabetes, in a dose response manner. A Mediterranean-type lifestyle, culturally adapted to non-Mediterranean populations, could help prevent type 2 diabetes and could be used to promote a healthy lifestyle in public health policies.Below is the link to the electronic supplementary material.Supplementary Material 1


This study aims to investigate the effectiveness of interventions to control myopia progression. In this systematic review, the primary outcomes were mean differences (MD) between treatment and control groups in myopia progression (D) and axial length (AL) elongation (mm).p < 0.001): highly aspherical lenslets , MiSight contact lenses , low dose atropine 0.05% , Biofinity +2.50 D , defocus incorporated multiple segments [DIMS]  and ortho-k lenses .The following interventions were found to be effective (Low-dose atropine 0.01% was not effective in reducing AL progression in two studies. Treatment efficacy with low-dose atropine of 0.05% showed good efficacy. Spectacles (HAL and DIMS) and contact lenses (MiSight and Biofinity) may confer a comparable treatment benefit compared to atropine, to slow myopia progression. The varn = 1,401), 2020  and 2021 . Thus, following the knowledge developments in this field is becoming more difficult. Several treatment options for myopia control have emerged in recent years. A few meta-analyses on myopia treatment efficacy were published in 2022  on myopia epidemiology, prevention, risk factors and myopia control, have been published. According to data from PubMed there were over 1,000 manuscripts published per year in 2019  were included if they compared interventions for slowing myopia progression in children with a treatment duration of at least 1 year. The primary outcomes of this study were the mean differences between treatment and control groups in myopia progression (D) and axial length elongation (mm) for the longer follow-up time reported in the RCT. The inclusion criteria were as follows: (1) RCT; (2) studies on treatment for myopia control published between 2019 and 2021; (3) children with myopia aged <18 years; (4) follow-up period of 1 year or more; (5) studies written in English language. Studies were excluded if (1) they had a retrospective component, were review papers or protocols, (2) they lacked the required outcome measures of this study, (3) refraction was measured without cycloplegia or not obtained using automated refraction, or (4) children were older than 16 years at baseline.in Situ NOT surgery AND humans AND Clinical Trial OR Randomized Controlled Trial OR Controlled Clinical Trial OR English Abstract OR Journal Article AND infant OR child OR adolescent.” We reviewed the references of all retrieved articles to identify articles not captured by the initial electronic search. Data was extracted and documented by one of the authors (CL) and verified by the other (AG). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist was used. We extracted the following information from each trial: type of intervention, follow-up duration, sample size and age, mean change in refraction and axial length.A previous Cochrane systematic review reviewed studies published up to 2018 . Thus, iThe methodological quality of RCTs was evaluated using the Cochrane Collaboration’s Risk of Bias Assessment tool (RoB v.2.0) . The metp-values or confidence intervals (CI), if needed  with 95% CI were illustrated with forest plots graphs. For the outcome myopia progression, a positive mean difference [MD] indicates that the intervention was better compared with the control group (less myopia progression). For the outcome axial length, a negative MD indicates that the intervention was better compared with the control group . As there was variation in sample sizes across the studies and more than 10 studies were included, we assessed publication bias using the funnel plot. A p-value of <0.05 was accepted as statistically significant. RevMan v. 5.4 software was used for the statistical analysis.A random effects analysis was performed to obtain conservative pooled estimates that took in consideration heterogeneity and sampling error. We also assessed heterogeneity with the 3.The electronic search identified a total of 3638 studies. Eleven studies reported both refraction and axial length outcomes, and 1 study only reported axial length.n = 2) or “high risk of bias” (n = 3) due to loss of follow-up or missing data. However, in some studies the treatment may not be completely masked due to the type of lenses or its effects, such as pupil dilation.There are some issues that should be noted, mainly related with the need to use data from intervention groups and the comparison with placebo groups: The Low-Concentration Atropine for Myopia Progression (LAMP) study was a RCT over 26 months, and we only used data from the first follow-up with 1-year treatment effects –13. The p = 0.03), for soft contact lenses was 0.39 D  and for spectacle lenses was 0.62 D  and the highest in the spectacle lenses subgroup (I2= 100%). The axial length elongation MD for atropine was −0.12 mm , for soft contact lenses was −0.18 mm , for spectacle lenses was −0.34 mm  and ortho-k lenses was −0.24 mm  and the highest in the soft contact lenses subgroup (I2= 88%).Most of the intervention methods slowed myopia progression compared to single vision spectacle lenses, single vision contact lenses or placebo. However, there were differences in treatment efficacy. The myopia progression MD for atropine was 0.29 D : highly aspherical lenslets , MiSight contact lenses , low dose atropine 0.05% , Biofinity +2.50 D , DIMS  and ortho-k lenses .Other interventions were also found to be effective but with lower effect sizes, such as extended depth of focus contact lenses, low dose atropine 0.025% or the esencia contact lens. The overall treatment effect was 0.37 D (95% 0.27–0.47) and −0.18 mm 95% −0.22 to −0.14). Low-dose atropine of 0.01% seemed to be the least effective method in controlling progression of myopia and axial length. Low-dose atropine 0.01% was not effective in reducing AL progression in 2 of the included studies , 22. The to −0.14For topical low dose atropine, control effects reported as percentage reduction in progression ranged from 27% (0.01%) to 67% (0.05%) for myopia progression and from 12% (0.01%) to 51% (0.05%) for axial length elongation. Spectacle lenses such as DIMS and aspherical lenslets were effective in slowing myopia progression  and axial elongation  in children compared with controls. MiSight contact lenses  and the Bifocal Lenses Biofinity +2.50 D also showed a significant slowing of myopia progression (reduction of 43%) and axial length elongation (reduction of 36%).Funnel plots for myopia progression and axial elongation shown in 4.This study investigated the effectiveness of interventions to control myopia progression. In addition, previous reviews such as the IMI-white papers  were updPrevious reviews concluded that there is high-level evidence to support the use of atropine to prevent myopia progression , 11, 32.clinicaltrials.gov website. Differences between Asian and European populations are likely, given the well-known effects of iris pigmentation in relation to cycloplegic agents, such as atropine. A report on the efficacy of atropine arising from racial differences showed that atropine is less effective in populations of European than East Asian origin (n = 14) had an increase in myopia progression and axial length during the COVID-19 lockdown year compared with the pre-lockdown year where the treatment was more effective  in slowing axial elongation. In Scandinavian children aged 6–12 years orthokeratology lenses reduced AL elongation by 0.24 mm after 18-months follow-up without vision-threatening adverse events . HoweverA study suggested that as a general goal, myopia control interventions should aim to provide a cumulative treatment effect of 1 D reduction to keep myopia below 6 D and axial length below 26 mm (40% less lifetime risk of developing myopic maculopathy) . NeverthThere are studies using combination of therapies for myopia control. Using topical 0.01% atropine with orthokeratology lenses has led to decreases in axial length elongation with most improvement during the first 6 months–1 year of treatment , 41. In Although most of the treatment protocols seem to control progression of myopia, a few factors should be considered when analyzing the results. Most studies that tested treatment efficacy, recruited a small number of children and some children were lost-to-follow-up. For example, in the 3-year RCT of MiSight Lenses only 75.5% of the children concluded the study (53 MiSight children and 56 controls), in the ortho-k study 30% of the subjects dropped out before the treatment was well established and in the extended depth of focus study 25% of children discontinued the treatment soon after lens dispensing and prior to the 1-month visit , 35, 36.www.pubmed.ncbi.nlm.nih.gov). Nevertheless, there is still no valid scientific criteria to decide when to initiate treatment based on progression and further research is necessary. Most pediatric ophthalmologists will treat children based on the rate of myopia progression  to 2022 . The decision must take into consideration that eye growth varies by season throughout the year, and it may be influenced by environmental factors , 49. ConI2 > 50%). Age was similar between studies and there was not sufficient data to explore how treatment varies with age.Our study has several limitations that should be highlighted. We only included 12 studies from 2019 to 2021. Although most studies were conducted in Asia, the target population varied. Both placebo, single vision spectacle lenses and single vision contact lenses were used as controls. These factors may have potential influence in our results. Thus, risk of bias cannot be excluded. There was high heterogeneity among each treatment regimen  HAL, MiSight contact lenses, low dose atropine 0.05%, Biofinity +2.50 D lenses, DIMS and ortho-k lenses were effective in the control of myopia progression; (2) Low dose atropine 0.025% and extended depth of focus contact lenses have also been found to be effective, but with lower effect sizes; (3) Low-dose atropine 0.01% was not as effective in reducing axial length progression according to some Asian studies. The recent data on new optical treatments, including soft contact lenses, DIMS and HAL, leads to optimism as these methods have shown considerable efficacy. Since they are much less invasive than alternatives such as orthokeratology and atropine, they are in principle likely to be preferable options. However, these results need to be confirmed in future as current knowledge is limited in the length of study periods and number of populations studied.AG was responsible for the concept and design of the study. CL performed the data acquisition and analysis, as well as interpreting the results and drafting the manuscript. CP participated in interpretation of results and revision of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Change in the composition of intestinal microbiota is associated with metabolic disorders such as gestational diabetes mellitus (GDM).To understand how the microbiota impacts the development of gestational diabetes mellitus, we profiled the intestinal microbiome of 54 pregnant women, including 27 GDM subjects, by employing 16S rRNA gene sequencing. Additionally, we conducted targeted metabolomics assays to validate the identified pathways with overrepresented metabolites.We evaluated the patterns of changing abundances of operational taxonomic units (OTU) between GDM and the healthy counterparts over three timepoints. Based on the significant OTUs, we inferred 132 significantly altered metabolic pathways in GDM. And identified two overrepresented metabolites of pregnancy hormone, butyrate and mevalonate, as potential intermediary metabolites of intestinal microbiota in GDM. Finally, we validated the impacts of the intestinal microbiota on GDM by demonstrating consistent changes of the serum levels of progesterone, estradiol, butyrate, and mevalonate in an independent cohort.Our findings confirm that alterations in the microbiota play a role in the development of GDM by impacting the metabolism of pregnancy hormones. This provides a novel perspective on the pathogenesis of GDM and introduces potential biomarkers that can be used for early diagnosis and prevention of the disease. PrimigrAbnormalities in the reprogramming of metabolic processes cause GDM. The latter is characterized by insulin resistance displayed in the second or third trimester, attributed to increased levels of certain pregnancy and placental hormones and pro-inflammatory factors . While mPregnancy hormones block insulin sensitivity hence partially causing GDM . NotablyThe hormonal changes during pregnancy are known to interact with the gut microbiota . Recent However, the intermediary metabolites mediating microbiota’s impact on pregnancy hormones and GDM remain unknown. Moreover, most prior studies on GDM’s microbiota are based on cross-sectional data with a case–control setting. Notably, changes in the gut microbiota of GDM subjects are often confounded with individual variation due to limited sample size.In this study, we hypothesize a metabolic mediation between gut microbiota and pregnancy hormones’ activity, affecting insulin and glucose metabolism during pregnancy. We profiled the gut microbiome at three different time points during pregnancy in both GDM subjects and healthy pregnant women. Our data suggest that gut microbiota is involved in GDM’s pathogenesis by influencing the biogenesis of steroid hormones through butanoate and mevalonate’s metabolic pathways.2.2.1.We selected 54 pregnant women admitted to the obstetrics department of the First Affiliated Hospital of Xiamen University between March 2018 and March 2019, who underwent regular prenatal check-ups, and gave birth in the hospital . The cliAll enrolled subjects received an oral glucose tolerance test (OGTT) at 24–28 weeks. After 3 days of a regular diet with at least 150 g daily carbohydrate intake, the subjects underwent 8-h fasting and were then examined. A standard 2-h OGTT was conducted and gestational diabetes mellitus (GDM) diagnosis was performed based on the International Association of Diabetes and Pregnancy Study Groups’ diagnostic criteria. Diagnostic cut-off values of fasting, 1-h, and 2-h plasma glucose levels were 5.1, 10.0, and 8.5 mmol/L. Women whose blood glucose values met or exceeded the above criteria at either time received a GDM diagnosis (GDM subjects). Age and body mass index (BMI)-matching pregnant women who tested negative were considered controls.For further validation using a targeted metabolomics assay, peripheral blood samples were collected from an independent cohort of 28 GDM subjects and controls .2.2.1 , t2 , and t3  of the 16S ribosomal RNA (rRNA) gene was used as an amplicon, and sequencing was conducted on the HiSeq platform. Genomic DNA samples of 30 ng were extracted and the corresponding fusion primers were configured for PCR amplification. The amplified products were purified using Agencourt AMPure XP magnetic beads and eluted in Elution Buffer. The resulting library was labeled and prepared for sequencing by using the Agilent 2,100 Bioanalyzer to detect fragment size and concentration. Qualified libraries were sequenced on the HiSeq platform based on the size of the inserted fragments. First, the original sequencing data was filtered for reads that were adaptor-contaminated, those containing N and low-complexity ones. Barcodes were removed before downstream analysis.2.3.2.https://github.com/qiime2/q2-feature-classifier). Alpha diversity was assessed based on observed species, Shannon index, and Simpson index using the phyloseq package in R-3.6.3. Beta diversity between GDM subjects and controls at different time-points was measured using principal component analysis (PCA) and linear discriminat analysis (LDA). The LDA analysis was done using ‘lda’ function in ‘MASS’ package, and visualized by‘ggord’ function in ‘ggord’ package. We also carried out LEfSe  analysis on Galaxy web application , grouped by condition .Raw sequence data were demultiplexed and quality-controlled using Quantitative Insights into Microbial Ecology . QIIME2 plugin DADA2 was used to denoise and produce operational taxonomic units (OTUs) at 100% similarity. We employed the Greengenes database (RRID:SCR_002830) as a reference to annotate OTUs. Taxonomic classification of marker-gene amplicon sequence was done using the sklearn classifier .We used log-ratio to measure each OTU’s difference of abundance between two adjacent time-points . Then, e2.4.n > 1). Next, we compared the most recurrent metabolites with those in known hormone-metabolic pathways  plugin in QIIME2pathways . Finallypathways  based onpathways .2.5.2.5.1.Blood metabolites were extracted from a 100 μL aliquot/subject and transferred to an Eppendorf tube. Subsequently, 300 μL of methanol were added, samples were vortexed for 5 min, and finally centrifugated at 12000 rpm at 4°C for 15 min.2.5.2.Eighty μL of the supernatant were transferred to an auto-sampler vial for gas chromatography–mass spectrometry (GC–MS) analysis. GC separation was conducted by Agilent 7890B GC System . Mass spectrometry was performed with an EI source with selected ion monitoring using an Agilent 5977A mass spectrometer . Ion source capillary temperature was 230°C.2.5.3.2) temperature = 250°C, gas (N2) flow = 11 L/min, sheath gas (N2) temperature = 400°C, sheath gas flow = 12 L/min, nebulizer = 35 psi.The Ultra-high-performance liquid chromatography (UHPLC) analysis was conducted using an Agilent 1,290 Infinity II series UHPLC System . Ion source parameters settings were as follows: capillary voltage = +3,000 V, Nozzle Voltage = +1,500 V, gas  parameters were optimized by directly injecting individual analytes’ standard solutions into the mass spectrometer’s API source. Of the two MRM transitions per analyte, the Q1/Q3 showing the highest sensitivity and selectivity were used as the MRM transitions for quantitative monitoring.Agilent MassHunter Workstation Software  was used for MRM data acquisition and processing.2.6.® XP Immunoassay System. Estradiol was measured by double-antibody sandwich chemiluminescene assay. Progesterone was measured by direct competitive chemiluminescene enzyme immunoassay.Plasma estradiol and progesterone levels were tested by ADVIA Centaur2.7.p > 0.05), a Student’s t-test was performed. Otherwise, a Wilcoxon test was used. Tests’ p values were adjusted using the Benjamini & Hochberg method, and the significance of the difference was determined by an false discovery rate (FDR) of 0.05.To evaluate the association between clinical characteristics and GDM, we performed a hypothesis test for each clinical feature in GDM subjects and controls. If a feature was normally distributed (Shapiro–Wilk test 3.3.1.p < 0.05). In early pregnancy, Hemoglobin (HB) levels in the controls (129.11 ± 10.05 g/L) were significantly higher than GDM subjects . Similarly to prior studies (p < 0.01).We established a prospective cohort to investigate the impacts of intestinal microbiota on GDM . Of the  studies , during 3.2.Firmicutes, Bacteriodetes, and Actinobacteria  analysis  on the top 100 OTUs with the highest mean absolute deviation (MAD>0) . Based oanalysis  for eachanalysis .1), GDM subjects and controls showed no significant difference in gut microbiota diversity . However, during the same stage, the Shannon and Simpson index found the microbial community more diverse in the GDM group. In the third trimester (t2), GDM subjects showed a significantly higher number of OTUs in gut microbiota than the controls , alpha-diversity showed no difference between GDM subjects and controls.During pregnancy’s early stages  in GDM subjects and controls. Notably, we identified 32 OTUs differently regulated in GDM subjects (FDR < 0.1)  . Among t3.4.2 vs. t1 and t3 vs. t2  based on the significant metabolic pathways’ activities followed by K-means clustering ,B. We obp < 0.05) formed three groups  and mevalonate-related metabolites (n = 16) were both highly represented  at t1, while they decreased to similarly low levels during late pregnancy  at tregnancy . Mevalon1 to t2   . The inc< 0.001) . Of note4.GDM is a common metabolic disorder during pregnancy, known to be associated with changes in the gut microbiome. However, to date, microbiota’s possible mechanisms and relevant metabolites in GDM subjects remain unclear. In this study, we identified a set of bacterial taxa associated with GDM’s onset in early and late pregnancy, and postpartum. We found that GDM-associated taxa are enriched in butanoate and mevalonate metabolic pathways in GDM subjects, resulting in increased levels of pregnancy hormones, further contributing to insulin resistance.Although the interaction between microbiota and GDM is widely reported, most of the current knowledge is based on cross-sectional design, making it challenging to establish causal relationships. The major advantage of the current study is based on self-controlled, time-dependent profiling of gut microbiota in GDM subjects and their healthy counterparts, via which we are able to obtain highly relevant changes in microbiota involved in the onset of GDM. This approach is more specific than cross-sectional design and augmented by stringent process for fecal specimen collection, preserving microbiota’s richness and enhancing the accuracy of the analysis. Our findings suggest that variations in gut microbiota contribute to the pathogenesis of GDM through individual hormonal metabolism, as represented by specific metabolites of pregnancy hormones. These results help differentiate the causal effects of microbiota on hormonal changes in GDM from other conflicting reports.Veillonellaceae was more abundant in GDM, and reported as a potential target for diabetes and hyperlipidemia management  was reported to be associated with type II diabetes  . Specifi1 in GDM subjects vs. controls, where butyrate levels remain stably low. Additionally, we report a set of significantly altered OTUs contributing to these pathways . The patients/participants provided their written informed consent to participate in this study.QL and QC conceived the study. SW, XL, LC, and YiZ (6th author) collected all clinical information and specimens. SW, LC, and YaZ conducted sample preparation. SW, XL, LC, and YaZ conducted the experiments. XL and JZ analyzed the data. QL, QC, XL, and YiZ (7th author) wrote the manuscript. All authors have given the consent for publishing the manuscript. All authors contributed to the article and approved the submitted version.This research was supported by the National Natural Science Foundation of China (No. 82272944), the Natural Science Foundation 1145 of Fujian Province (No. 2021J05298) and Xiamen Medical and Health 1146 Science and Technology Project (No. 3502Z20194011).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Global prevalence of osteoporosis and fragility fractures is increasing due to the aging population. Proximal femoral fractures are among the most common orthopedic conditions in elderly that significantly cause health deterioration and mortality. Here, we aimed to evaluate the mortality rates and risk factors, besides the functional outcomes after these injuries.In a retrospective cohort study, all patients admitted with a femoral neck or intertrochanteric fracture between 2016 and the end of 2018 were enrolled in this study. Medical records were reviewed to include patients over 60 years of age who had a proximal femoral fracture and had a complete medical record and radiographs. Exclusion criteria included patients with pathological fractures, cancer under active treatment, follow-up loss, and patient access loss. Demographic and clinical features of patients alongside the details of fracture and patient management were recorded and analyzed. In-hospital and post-discharge mortalities due to included types of fractures at one and 12 months were the primary outcome. Modified Harris Hip Scores (mHHS) was the measure of functional outcome.A total of 788 patients including 412 females (52.3%) and 376 males (47.7%) with a mean age of 76.05 ± 10.01 years were included in this study. Among patients, 573 (72.7%) had an intertrochanteric fracture, while 215 (27.3%) had a femoral neck fracture, and 97.1% of all received surgical treatment. With a mean follow-up of 33.31 months, overall mortality rate was 33.1%, and 5.7% one-month and 20.2% 12-months rates. Analysis of 1-month mortality showed a significant mortality difference in patients operated after 48 h of fracture (p = 0.01) and in patients with American Society of Anesthesiologists (ASA) scores of 3–4 compared to ASA scores of 1–2 (p = 0.001). One-year mortality data showed that the mortality rate in femoral neck fractures was lower compared to other types of fracture. Surgical delay of > 48 h, ASA scores of 3–4, and treatment by proximal femoral plate were associated with shorter survival. The overall mean mHHS score was 53.80 ± 20.78.We found several risk factors of mortality, including age ≥ 80 years, a > 48-hour delay to surgery, and pre-operative ASA scores of 3–4 in patients with proximal femoral fracture. Furthermore, the use of a proximal femoral plate was a significant risk factor for mortality and lower mHHS scores. Due to the aging population, the worldwide prevalence of osteoporosis is rising, and correspondingly, the incidene of fragility fractures are increasing . NationaProximal femoral fractures significantly contribute to health deterioration and long-term morbidity and mortality. The arduous rehabilitation, functional decline, and reduced quality of life affect patients’ independence and livelihood , 8. AddiIn line with the improvements in the prevention and medical care of proximal femoral fractures, orthopedic treatment has also undergone a dramatic shift in the past decades . As our Although several large-scale studies have been performed in developed countries, there is a paucity in the literature regarding the outcomes of proximal femoral fractures in developing countries like Iran , 14. ParIn an IRB-approved retrospective cohort study, our institutional hospital information system (HIS) was queried for all patients admitted with a femoral neck or intertrochanteric fracture diagnosis from January 1, 2016, through December 31, 2018 to one referral orthopedic surgery center affialited with Kermanshah University of Medical Sciences, Kermanshah, Iran. Medical records were reviewed to include patients over 60 years of age who had a proximal femoral fracture and had a complete medical record and radiographs. The 60 years age cut-off for inclusion of the elderly population with proximal femoral fracture in this study was chosen based on the prevalence of this type of fracture in Iranian elederly population. Exclusion criteria included patients with pathological fractures, cancer under active treatment, follow-up loss, and patient access loss.Basic patient information including sex, mechanism of injury, past medical history, American Society of Anesthesiologists (ASA) score status, the delay from fracture to surgery, type of anesthesia, surgical technique and the utilized hardware, the duration of hospital stay, and discharge status of patients were recorded. The admission radiographs were reviewed on the local picture archiving and communication system (PACS), and each fracture was classified according to the 2018 AO fracture and dislocation classification . AssociaIn-hospital and post-discharge mortalities at one and 12 months due to included types of fractures were identified and reviewed as the primary outcome in this study. Additionally, follow-up visits were reviewed to determine whether the patient recovered or died. The two time points of one-month and one-year for patient follow-up were chosen based on a literature review and similar studies in the field as most of the adverse outcomes following surgery of this condition in elderly population happen in the first month after surgery and also the first-year assessment is a crucial time-point in surgical outcomes investigations. Modified Harris Hip Scores (mHHS), collected at the latest clinic visit, were also recorded . FurtherStatistical calculations and analyses were performed using SPSS Statistics for Windows, Version 25.0 (IBM). Statistically significant p for type I error was set at < 0.05. Descriptive statistics were used to calculate mean and standard deviation (± SD). Categorical variables were reported as frequency and percentage. Student’s t or Mann-Whitney U tests were used to compare means depending on the normality of data distribution. Pearson’s chi-square or Fischer’s exact test compared categorical variables. Kaplan-Meier survival analysis was performed to estimate the probability of survival at a specific time after the fracture (1-month and one-year periods), and the log-rank test was used to compare survival between groups. The Cox proportional hazard models were used to identify the predictors of mortality. Independent variables in the recruited models included age, sex, AO type of fracture, time from admission to surgery, orthopedic hardware utilized, and ASA score. Crude and adjusted hazard ratios with 95% confidence intervals (CI) were calculated for each variable.The study protocol was reviewed and approved by the Research Ethics Committee of Kermanshah University of Medical Sciences (IR.KUMS.REC.1398.1175). This study was performed following the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans, and the human subjects’ privacy rights were respected.A total of 1004 cases with a proximal femoral fracture were identified during the study period. Of these, 889 patients satisfied the inclusion criteria. After excluding 101 (11.4%) patients who were lost to follow-up or had incomplete records/radiographs, a total of 788 patients including 412 females (52.3%) and 376 males (47.7%) with a female to male ratio of 1.1 were included in the final analysis  sustained a fracture after a simple fall, and only 6% of fractures resulted from high-energy trauma. Concomitant fractures were diagnosed in 117 patients (15.0%), most commonly a distal radius or a humerus fracture. A history of previous hip fracture was present in 74 patients (9.5%). In 462 patients (59.4%), at least two comorbidities were present, the most common of which were dyslipidemia, diabetes mellitus, ischemic heart disease, and Alzheimer’s disease  had an intertrochanteric fracture, while 215 (27.3%) had a femoral neck fracture. According to the AO classification, 180 fractures (22.8%) were considered type 31A1, 233 (29.6%) were 31A2, 160 (20.3%) were 31A3, and 215 (27.3%) were 31B. Patients with a femoral neck fracture were significantly younger than those with an intertrochanteric fracture (p = 0.01).Overall, 23 patients (2.9%) did not undergo surgery and were treated non-operatively, with the remaining 765 patients (97.1%) received surgical treatment in one of the forms of screws, dynamic hip screws (DHS), open reduction and internal fixation (ORIF) with a proximal femoral plate, or bipolar hemiarthroplasty , and survival assessment was determined based on health status on 1/1/2020. Overall mortality rate was 33.1% (262 patients), of which 6 deaths (2.3%) were in-hospital, 45 (17.2%) occurred during the first post-fracture month, and 159 (60.7%) happened during the first year following fracture. Although the mean overall survival was slightly higher in male patients , men’s survival was lower in the ≥ 80-years age group (30.8 vs. 33.5 months).Analysis of 1-month mortality showed a significant mortality difference in patients operated after 48 h of fracture (p = 0.01) and in patients with ASA scores of 3–4 compared to ASA scores of 1–2 (p = 0.001). One-year mortality data showed that the mortality rate in femoral neck fractures was lower compared to other types of fracture. Surgical delay of > 48 h, ASA scores of 3–4, and treatment by proximal femoral plate were associated with shorter survival . Additionally, delayed surgery p = 0.02) and an ASA score of 3–4 (p = 0.04) significantly increased the mortality risk, while treatment with bipolar hemiarthroplasty decreased the risk of mortality (p = 0.001). On multivariable Cox regression analysis, age ≥ 80 years, ORIF with a proximal femoral plate, delayed surgery, and ASA scores of 3–4 were risk factors of mortality, while bipolar hemiarthroplasty reduced the risk of mortality . The results indicated that a bipolar hemiarthroplasty decreased the risk of mortality with a hazard ratio of 0.61 , while age ≥ 80 years, delayed surgery, ORIF by a proximal femoral plate, and ASA scores of 3–4 were significant risk factors of mortality , which are also important in making treatment decisions and the patients’ long-term function. Despite these limitations, we have studied a large cohort of patients with hip fractures, and with minimal loss to follow-up, managed to report mortality up to 1 year after fracture with the underlying risk factors. Reporting the functional outcomes in addition to the mortality rate is another strength of this study.To conclude, in the study of 788 Iranian elderly patients with a hip fracture, we found 1-month and 1-year mortality rates 5.7% and 20.2%, respectively. We found several risk factors of mortality, including age ≥ 80 years, a > 48-hour delay to surgery, and pre-operative ASA scores of 3–4. Furthermore, the use of a proximal femoral plate was a significant risk factor for mortality and lower mHHS scores, while a bipolar hemiarthroplasty was associated with a decreased risk of mortality and higher mHHS scores.


C. esculenta tuber extracts, a novel nutraceutical in benign prostate hyperplasia in a rat model.This study was motivated by the increasing global incidence of benign prostatic hyperplasia (BPH) and the promising potential of nutraceuticals as complementary therapies in ameliorating its burden. We report the safety profile of C. esculenta (ECTECE) or hexane, dichloromethane, butanone, ethyl acetate and aqueous fractions of ECTECE respectively for a period of 28 days.In this study, forty-five male albino rats were randomly assigned to 9 groups of 5 rats each. Group 1  received olive oil and normal saline. Group 2 (BPH untreated group) received 3 mg/kg of testosterone propionate (TP) and normal saline, and group 3 (positive control) received 3 mg/kg of TP and 5 mg/kg of finasteride. Treatment groups 4, 5, 6, 7, 8, and 9 received 3 mg/kg of TP and a middle dose (200 mg/kg) of LD50 of ethanol crude tuber extract of C. esculenta fractions.The negative controls showed a significant (p < 0.05) increase in mean relative prostate weight (approximately 5 times) as well as a reduction in relative testes weight (approximately 1.4 times less). There was no significant (p > 0.05) difference in the mean relative weights of most vital organs: liver, kidneys, and heart. This was also observed in hematological parameters: RBC, hemoglobin, HCT, MCV, MCH, MCHC, and platelets counts. In general, we note that the effects of the well-established drug finasteride on the biochemical parameters and histology of selected organs are comparable to those of This study demonstrates that C. esculenta tuber extracts provide potentially safe nutraceutical if applied in the management of benign prostate hyperplasia based on a rat model. The increasing trend of life expectancies across the world  has beenConventional pharmacological management of BPH usually involves use of alpha-blockers comprising alpha-adrenoceptor antagonists, or 5-alpha-reductase inhibitor or a combination of both , 17, 19.C. esculenta  Nigeria is among the neglected but important foods across the African continent [C. esculenta in-vitro and in-vivo have previously been reviewed by Prajapati et al. (2011) [C.esculenta has novel tumor specific anti-cancer activities on rat YYT colon cancer cell line [C. esculenta decreases obsessive-compulsive disorder in mice. Some studies have also demonstrated the anti-diabetic properties of C.esculenta tuber and leaves in rat model [There are vast quantities of unexplored novel phytotherapeutic agents from commonly consumed foods that could provide better options for management and/or prevention of BPH , 25. Sucontinent –29traditontinent . Medicinell line . Furtherell line  reportedat model –37.C. esculenta contains anti-inflammatory agents that may remediate BPH associated inflammation. Studies by Nzebang et al.(2018) [C. esculenta leaves infected by Phytophthora colocsiae would be no major health risk with estimated LD50 of more than 4000 mg/kg in rats Although our previous studies have demonstrated that the ethanol crude extract of C. esculenta has the capacity to reduce prostate weight, total protein as well as serum concentration of prostate specific antigen [C. esculenta in crude and semi purified form for the management and / or prevention of BPH.Previous studies by Eleazu and others  based onl.(2018)  suggest Collection and Identification of Plant Materials: Mature (6–8 months old) fresh tubers of C. esculenta were collected with permission from the selected farmers and local authorities including agricultural extensions officer as guided by the Office of Research, Innovation and Institutional Ethics Committee of Ebonyi state university, Nigeria (EBSU/BCH/ET/21/001). This was done in accordance with guidelines from the Nigerian Federal Environmental Protection Agency and the International Union for Conservation of Nature (IUCN) policy on Research Involving Species at Risk of Extinction. This was followed by authentication by a taxonomist, Professor S.O. Onyekwelu at the department of Applied Biology, Ebonyi state University (Colocasia esculenta (L) Schott, Family Araceae, common name: Cocoyam Local name; Ede ofe, voucher number: EBSU-H-206, Department of applied biology herbarium, Ebonyi State university, Nigeria ; Herbarium curator, Mr. Nwanko Onyebuchi Ephraim.C. esculenta tubers were washed, boiled, peeled, sliced into chips, air-dried to a constant weight at a room temperature and processed into flour.C. esculenta were extracted with 8 L of 50% ethanol (Emsure®) overnight in a big stopper bottle with occasional stirring at room temperature. It was then sieved using muslin cloth. The filtrates were air dried for 24 h to get the ethanol (crude) extracts.The powdered tuber 1280 g of The crude natural ethanol product was extracted with solvents of increasing polarity, first, hexane (Blulux), dichloro methane (UNILAB), ethyl acetate (UNILAB) and butanol  which depended on the chemical and physical nature of the target compounds.This study was done under the supervision and approval by the Office of Research, Innovation and Institutional Ethics Committee of Ebonyi state university, Nigeria (EBSU/BCH/ET/21/001). All procedures for animal studies were performed following guidelines and legislations consistent with the National Institute of Health Guide for the Care and Use of Laboratory Animals   as well ad libitum. All animals were humanely sacrificed using halothane.The study used 45 male Wistar albino rats of about 6 weeks old which were sourced from the Animal House of Ebonyi state university, Nigeria. They were kept in stainless cages (size: 16 × 9 = 144inches) in a well-ventilated animal house. They were acclimatized for seven days under good laboratory conditions  before the start of the experiments. All animals were allowed free access to standard rodent chow and water We induced Benign Prostatic Hyperplasia in rats using testosterone propionate (TP) , 35. Theet al. (2021). Group 4 had rats which were treated with 200 mg/kg body weight (b.w) of ethanol crude tuber extract of C. esculenta (ECTECE). Group 5 had rats that were treated with 200 mg/kg b.w of n-hexane fraction (HF). Group 6 had rats that were treated with 200 mg/kg b.w of dichloromethane fraction (DCMF). Group 7 had rats that were treated with 200 mg/kg b.w of ethyl acetate fraction (EAF). Group 8 had rats that were treated with 200 mg/kg b.w of butanone fraction (BF). And finally, group 9 had rats that were treated with 200 mg/kg b.w of aqueous fraction (AF). Oral administration of the extract or fractions or finasteride was done using oral gavage and all animal diets were provided ad libitum.The rats were grouped as follows, with five rats in each group: Rats in group 1  received subcutaneously 1ml of olive oil. BPH was induced in groups 2–9 with 3 mg/kg TP subcutaneously. Group 2 (BPH untreated group) was not treated. Group 3 (Finasteride group) had rats which were treated with 5 mg/kg finasteride. The middle dose of 200 mg/kg was used for this study as reported by Eleazu 0c for two days after which a complete blood count was performed.After 28 days, the rats were fasted overnight, sacrificed after being anesthetized with halothane and dissected to collect vital organs which included liver, kidney, heart as well as prostate and testes. All organs were weighed and immediately placed in 10% neutral buffered formalin for fixation until histopathological examination. We also collected blood samples from animals by cardiac puncture, using 5ml syringes into EDTA vacutainers for complete blood count. Anticoagulated blood samples were stored at 2 to 8Total protein was determined using the method in Tietz . The bodFor complete blood count, we used a five-part differential fully automated analyser, Sysmex XN550, Japan. Sample tubes with caps closed were placed in sample adapters. We then used the start/stop switch to execute automatic sample mixing, aspiration, analysis and printing of results.The histopathological examination of the organs was done using standard procedures . TissuesFor the complete blood count, we used control blood and normal samples to monitor daily variation. We also employed the XN check levels 1, 2 and 3. In addition, we followed manufacturer’s instruction and standard operating procedures for equipment start-up, self-check, as well as sample preparation and loading.For histopathology, we ensured use of standard operating procedures and all stains and reagents were used after filtration. Examination of slides was done by two qualified pathologists and in cases of a discrepancy, a third pathologist was involved.Data were analyzed using Prism software  to determine statistical significance. Dunnet ANOVA test was used to compare the mean values of the individual groups and control at P < 0.005. The results are shown as mean ± SD of 5 rats per group.Colocasia esculenta (ECTECE), hexane fraction (HF), dichloromethane fraction (DCMF), butanone fraction (BF), Ethyl acetate fraction (EAF) and aqueous fraction (AF) in male rats significantly (p < 0.05) reduced the level of serum PSA in comparison to the normal control group with no significant change (p > 0.05) in total protein concentration in the serum. Interestingly, significant (p < 0.05) reductions in the level of serum PSA were observed in all the fractions except butanone fraction when compared with the normal control group  elevated serum PSA level in Table up Table .C. esculanta fractions induced changes in serum total protein which were comparable to the standard treatment (finasteride)  treatment group (6.18 ± 0.19 mg/dl). This was also observed in mean total body weight of the animals  Fig. . The meaals Fig. B. The meals Fig. A on treaC. esculenta (ECTECE), hexane fraction (HF), dichloromethane fraction (DCMF), butanone fraction (BF), ethyl acetate fraction(EAF) and aqueous fraction(AF) showed no significant (p < 0.05) difference on the relative organs weights  difference on the relative liver, kidneys and heart weights although there was a significant (p < 0.05) reduction in mean relative testes weight by approximately 1.4 times less , hexane fraction (HF), dichloromethane fraction (DCMF), butanone fraction (BF), Ethyl acetate fraction (EAF) and aqueous fraction(AF) in male rats showed no significant(p < 0.05) difference on the haematological parameters as shown in Fig. + AF  difference on RBC, haemoglobin, HCT, MCV, MCH, MCHC and platelets levels ). Co-administration of TP and ethanol crude Tuber extract of C. esculenta (ECTECE), hexane fraction (HF), dichloromethane fraction (DCMF), butanone fraction (BF), Ethyl acetate fraction (EAF) and aqueous fraction (AF) in male rats significantly (p < 0.05) elevated the counts of WBC, monocytes and lymphocytes with significantly (p < 0.05) reduced neutrophil, eosinophil and basophil counts as shown in Fig. Administration of TP in male Wistar albino rats significantly p > 0.05) reduced the WBC, monocytes and lymphocytes counts with a significant (p < 00.05) elevation on the counts of neutrophils, eosinophil and basophiles -4-methylimidazolium, hexanedioic acid, bis(2-ethylhexyl)ester and 3,5-di-t-butyl phenol. This is in addition to the high phenolic compounds content composed of Gallic Acid, Quercetin and Catechin [4, anti-androgenic, lipoxygenase inhibitory and hypocholesterolemic properties ( organic compounds such as 9-octadecenoic acid ) [This study was aimed at investigating the safety profile of Catechin , 44. Manc acid ) .C. esculenta (ECTECE) or hexane, dichloromethane, butanone, ethyl acetate and aqueous fractions of ECTECE which suggest bioactivity by the crude extract and fractions of ECTECE in halting the breakdown of tissue proteins.Data from the biochemical and hematological parameters of our study do not show evidence of toxicity. However, we observed decrease in body weights of the treatment group. This is suggestive of a potential cellular response to arrest BPH through breakdown of tissue proteins . In addiMore to this the elevated prostate weights as observed in the BPH untreated group could have resulted from pathological alterations in the prostatic tissue linked to BPH , 45. HowP < 0.05) of hepatic AST, ALT, ALP and serum proteins and albumin, but had significant reduction (P < 0.05) of blood glucose, serum urea, creatinine, amylase, lipase, AST, ALT and ALP compared with the diabetic control rats also support the safety of cocoyam in the management of diseases.According to the present study, non-significant (p > 0.05) differences in relative liver, kidney and heart weights were observed in both BPH untreated group and the treated groups. This non-significant difference in the relative’s organs weight may demonstrate nontoxic effect of the crude extract of cocoyam and fractions to the organs , 47. ThiReduction in the testes weight in the BPH untreated group as observed in this study could be as a result of exogenous effect of TP as a potential male contraceptive . On the esculenta leaves in the reduction of fat adipose tissues and its ameliorative effect on HFD-induced liver damage.Increased counts of neutrophils, eosinophils, basophils and reduction in white blood cell, monocytes and lymphocytes counts; no significant (p > 0.05) difference on RBC, haemoglobin, HCT, MCV, MCH, MCHC and platelets counts could be attributed to the BPH induction in BPH untreated group. The treatment with crude extract of cocoyam tuber and fractions modulated the immune and haematological parameters showing the safety of the cocoyam extract and fractions. This result is similar with Princewill-Ogbonna et al. (2016)  which reC.esculenta has a potential for use as a nutraceutical in benign hyperplasia thus we recommend further studies on mechanism and the active components present. Given that C.esculenta is a commonly consumed food in many African countries it would be nice to do epidemiological studies in these areas with higher consumption of this important food. And probably be subjected to clinical trials aimed complementing standard therapies as a nutraceutical.Our results suggest that


Non-accidental trauma (NAT) is deadly and difficult to predict. Transformer models pretrained on large datasets have recently produced state of the art performance on diverse prediction tasks, but the optimal pretraining strategies for diagnostic predictions are not known. Here we report the development and external validation of Pretrained and Adapted BERT for Longitudinal Outcomes (PABLO), a transformer-based deep learning model with multitask clinical pretraining, to identify patients who will receive a diagnosis of NAT in the next year. We develop a clinical interface to visualize patient trajectories, model predictions, and individual risk factors. In two comprehensive statewide databases, approximately 1% of patients experience NAT within one year of prediction. PABLO predicts NAT events with area under the receiver operating characteristic curve (AUROC) of 0.844 (95% CI 0.838–0.851) in the California test set, and 0.849 (95% CI 0.846–0.851) on external validation in Florida, outperforming comparator models. Multitask pretraining significantly improves model performance. Attribution analysis shows substance use, psychiatric, and injury diagnoses, in the context of age and racial demographics, as influential predictors of NAT. As a clinical decision support system, PABLO can identify high-risk patients and patient-specific risk factors, which can be used to target secondary screening and preventive interventions at the point-of-care. NAT is also difficult to predict, due to heterogeneity of presentation, complex and rapidly changing epidemiology7, and concentration in historically understudied populations8. Screening for NAT during clinical encounters is not routine, and may be associated with both provider and patient discomfort9. Therefore, the effects of routine screening for NAT have not been well-demonstrated, except in special populations10.Non-accidental trauma (NAT) comprises a heterogeneous set of diagnoses, including many forms of assault, abuse, maltreatment, and neglect. NAT is a leading cause of injury and death, particularly for children and adolescents12. Prior research has also associated NAT with specific clinical contexts, as in children with fracture patterns suggestive of NAT13. One previous modeling study evaluated a Bayesian classifier for predicting a variety of NAT diagnoses14. This approach used a flattened representation of patient histories, precluding use of information about the sequence and tempo of visits. More generally, prior studies of NAT prediction have not shown external validation15, an important assessment of the ability of a model to generalize to different patient populations and clinical environments.An automated screening algorithm using existing electronic health record (EHR) data could enable universal screening for high-risk patients without requiring additional clinical resources or imposing a social burden. A limited number of studies have evaluated the predictability of NAT. Most research has been restricted to identification of broad risk factors, such as psychiatric illness and prior involvement in violence16 have achieved state-of-the-art performance in multiple domains18, including disease prognostication20. Bidirectional Encoder Representations from Transformers21 (BERT) is a popular architecture that can effectively learn long-range patterns from sequence data, making it promising for the prediction of longitudinal outcomes. Such models benefit from general pretraining on massive datasets, which improves performance when the model is subsequently fine-tuned on a specific prediction task21. However, previous studies of disease prediction have focused primarily on generic pretraining methods such as masked language modeling (MLM)19 and contrastive learning22, which are not specifically tailored to the characteristics of medical trajectories. One study used a domain-adapted pretraining task for predicting prolonged length-of-stay20, but not specific diagnoses or outcomes.Deep learning models based on transformer architecturesOur goal is to develop a clinically relevant prediction framework for NAT and other challenging diagnoses, using longitudinal patient trajectories containing patient demographics, diagnoses, and procedures. Using statewide data on millions of encounters, we pretrain a BERT-based model adapted for longitudinal diagnostic prediction with a multitask pretraining objective. By flexibly predicting the temporal relationships of diagnoses, we aim to develop a generally applicable base model for diagnostic forecasting. We fine-tune and externally validate our model for the prediction of NAT. We compare model performance to a traditional machine learning algorithm and a previously published BERT-based model. We develop an interactive clinical interface for understanding model predictions and individual risk factors, which can be implemented as a clinical decision support system.We developed Pretrained and Adapted BERT for Longitudinal Outcomes (PABLO), which we fine-tuned and validated for the prediction of NAT within one year. Cohort creation for pretraining, development, test, and external validation datasets is shown in Fig. −2 (95% CI 8.70 × 10−2 − 10.32 × 10−2). For prediction of first NAT , PABLO had an AUROC of 0.820 (95% CI 0.812–0.828) and AUPRC of 4.80 × 10−2 (4.38 × 10−2 − 5.37 × 10−2). ROC curves are shown in Supplementary Fig. In each cohort, 1% of patients experienced NAT within one year of prediction Table . On the −2 (95% CI 6.54 × 10−2 − 7.05 × 10−2). In external validation of first NAT prediction, PABLO had an AUROC of 0.830 (95% CI 0.827–0.833) and AUPRC of 3.64 × 10−2 (3.51 × 10−2 − 3.79 × 10−2). ROC curves for external validation are shown in Supplementary Fig. p < 0.05, evaluated via bootstrapping with 10,000 resamples  and AUPRC of 6.78 × 10p < 0.05. Flagging the same proportion of visits, PABLO would detect 98.4% more cases of actual NAT than logistic regression, 7.1% more cases than XGBoost, and 4.3% more cases than BEHRT.Supplementary Table We evaluated prediction performance on demographic subgroups  in the CA test set and 216 days IQR 135-291) in the FL external validation dataset. Prediction confidence declined with increasing lag between index visit and NAT, likely reflecting the fact that a longer delay between prediction and event implies more unobserved information influencing the event (and therefore weaker predictions). For NAT cases, the mean calibrated predicted probability of NAT declined by 1% for each additional 100 days between prediction and event  and a state-of-the-art deep-learning model (BEHRT). These improvements may reflect our model’s attention to the temporal structure of patient trajectories, which is generally discarded in conventional algorithms using flattened representations of patient histories24). In this study, flagging the top 1% of patients by predicted NAT risk yields overall PPVs from 0.121–0.169, meaning that a minority of patients flagged as high risk would in fact experience observed NAT. This suggests that models like PABLO are best used as an automated primary screen to flag high-risk patients for detailed, secondary screening, rather than as a diagnostic test on its own. Notably, PABLO’s PPV significantly outperformed comparator models at the same risk threshold, detecting 4.3% to 98.4% more cases of actual NAT than alternative models. Given the morbidity and mortality of undiagnosed NAT, we view these gains as potentially clinically meaningful. PPV was substantially higher for patients with more visits on record, who both had more informative trajectories, and were more likely to experience NAT. Indeed, many previous studies of disease prognostication have restricted cohorts to patients with large numbers of visits (ranging from five19 to 25 visits22), discarding the majority of the patient population with relatively few visits. By contrast, we studied all patients with three or more visits, a more representative but more sparsely documented population. In practice, because the proportion of patients to approach for secondary screening will vary with the resources and goals of the implementing institution, we report test characteristics at various thresholds.Predicting NAT is complex, requiring identification of individuals at high risk of a rare and heterogenous outcome (NAT within one year) from an extremely large and diverse screening pool . Therefore, while our model achieved high AUROCs, AUPRCs were comparatively lower. Low PPV is typical in the prediction of rare outcomes  was very similar to that in the CA test set. High performance in external validation may result from the large volume of multi-hospital data we used during training, the generality of the pretraining tasks, and the ubiquitous character of the demographic, diagnostic, and procedural data on which our approach relies. Nonetheless, model calibration will be necessary to achieve appropriate probability estimates in any target population.25 or pulmonary embolus26) consist of a small number of features reflecting major risk factors. We demonstrate that individual predictions from PABLO  can be explained using feature attributions, producing concise explanations of individual risk. These attributions reflect known risk factors for NAT. For instance, pregnancy and the postpartum period are associated with elevated risk for homicide27, which was reflected in the model’s attention to pregnancy-related diagnoses. Drug and alcohol use has also been correlated with adolescent homicides28, which was reflected in our model’s high attributions for substance use diagnoses, particularly for younger patients. Inspection of individual cases also revealed instances of the model omitting clinically relevant risk factors, such as repeated asphyxia and dyspnea diagnoses or non-assault injury diagnoses. These omissions were primarily observed in lower-risk demographic segments , indicating the importance of demographic context to model predictions. Review of false positive trajectories indicated an over-weighting of potentially irrelevant attributes, such as dermatologic conditions and idiopathic musculoskeletal pain. These patterns may relate to the representational adjacency of these diagnoses to true NAT risk factors in the model. We propose that concise, interactive visualizations of model predictions and attributions for individual patients will allow clinicians to rapidly assess the plausibility of a given prediction.Interpretability is crucial to the clinical utility of a predictive model. Clinicians may have higher confidence in a model that corroborates their own knowledge of risk factors, and many of the most commonly used predictive tools  visit and inpatient admission records in the state of California (CA) from October 1, 2015 (the beginning of the ICD-10 coding period) to December 31, 2019, totaling 40,895,601 encounters from 446 facilities. The dataset used for external validation was obtained from the Agency for Healthcare Research and Quality, which included all ED visit and inpatient admission records in Florida (FL) from October 1, 2015 to December 31, 2019, totaling 31,771,462 encounters from 217 facilities.34.We built datasets around a single prediction point for each patient trajectory, which we refer to as the “index visit”: the patient’s last visit in 2018, so as to observe at least one year of data after the point of prediction. The pretraining dataset contained all patient trajectories from CA with two or more visits up to the index visit (for trajectories including at least one visit in 2018), or with two or more visits from 2015 to 2017 (for trajectories without visits in 2018). For pretraining, we used the category of the principal diagnosis from last visit as a multiclass label, with diagnosis categories defined by the Clinical Classification Software Refined (CCSR) for ICD-10-CM diagnoses14, and included the ICD-10 codes referenced in Supplementary Fig. The fine-tuning and external validation datasets included all patient trajectories from CA and FL, respectively, with three or more visits, at least one occurring in 2018. We required at least three visits for fine-tuning and validation, compared to two visits for pretraining, so that fine-tuning and validation trajectories would retain longitudinal information  after removing the final visit during prediction. We defined NAT cases as trajectories including one or more NAT diagnoses within 365 days following the index visit Fig. . Diagnos35.Demographic attributes were extracted from the index visit, including age (grouped by decade), sex, race, and primary visit payor. Each visit included ICD-10 codes for the principal diagnosis, up to 24 auxiliary diagnoses, and up to five external cause of injury codes, which classify injuries by mechanism and intent. ICD-10 codes were truncated to the first three characters, to group similar diagnoses. Visits also included Current Procedural Terminology (CPT) codes for procedures performed in the ED and ICD-10 codes for procedures associated with hospital admissions, both of which were translated into a common lexicon of 231 procedural categories using CCS for Services and Procedures21. We fixed trajectories to 512 embedded features: longer trajectories were truncated to include information from only the more recent visits, and shorter trajectories were padded to a length of 512 using a padding token ignored in model computations. These trajectories, represented as 512 × d matrices, were then fed into a BERT encoder.We developed PABLO as follows. All features of a patient trajectory were converted into dense embedding representations Fig. . Each div visits, all codes from the last visit v were masked, and the previous v - 1 visits were used to predict the clinical category of the principal diagnosis of visit v using a feed-forward layer over the pooled model output  with default settings where not otherwise specified. All training was conducted on an NVIDIA Tesla V100 GPU.For both pretraining and fine-tuning, we determined model hyperparameters via Bayesian optimization  to visualize the d-dimensional feature embeddings with over 1000 occurrences in the development dataset. To interpret model predictions, we used the integrated gradients technique43, which was chosen for its axiomatic properties and implementation efficiency. Unlike standard statistical models whose measures of feature importance are averaged across the population, transformers weight features based on contextual interactions. This contextual weighting allows for unique, patient-specific attributions for each feature. We calculated attribution scores for trajectories in the CA test dataset using the Captum library (0.6.0).We used Using PABLO’s predictions and feature attributions, we developed an interactive clinical interface to visualize patient trajectories and risk factors. To preserve anonymity, we generated six representative true positive trajectories by identifying subpopulations of similar NAT cases in the CA test set and selecting representative examples from each subpopulation. 30% of features in each trajectory were then randomly resampled from their respective cohorts. We excluded from visualization diagnoses with fewer than 1000 occurrences. We generated analogous trajectories for false positive and false negative predictions, selecting the non-NAT cases with the highest predicted probabilities , and the NAT cases with the lowest predicted probabilities .Datasets were extracted and joined using R (4.0). Analyses were performed using Python (3.7.12). Data processing and cohort statistical analyses were performed using NumPy (1.21.6), pandas (1.3.5), and scikit-learn (0.24.2). Model development was performed using scikit-learn (0.24.2), XGBoost (1.5.0), PyTorch (1.10.1), Transformers (4.21.2), and Weights & Biases (0.13.2). Model analyses were performed using scikit-learn (0.24.2) and Captum (0.6.0). Visualizations were created using Matplotlib (3.4.2), R (4.0), and D3.js (7.8.2).Further information on research design is available in the Supplementary InformationReporting Summary