Trypanosoma cruzi (Tc) and Leishmania spp. (Lspp). In this study, we performed an in silico analysis of the CaN subunits, comparing them with the human (Hs) and looking their structure, post-translational mechanisms, subcellular distribution, interactors, and secretion potential. The differences in the structure of the domains suggest the existence of regulatory mechanisms and differential activity between these protozoa. Regulatory subunits are partially conserved, showing differences in their Ca2+-binding domains and myristoylation potential compared with human CaN. The subcellular distribution reveals that the catalytic subunits TcCaNA1, TcCaNA2, LsppCaNA1, LsppCaNA1_var, and LsppCaNA2 associate preferentially with the plasma membrane compared with the cytoplasmic location of HsCaNAα. For regulatory subunits, HsCaNB-1 and LsppCaNB associate preferentially with the nucleus and cytoplasm, and TcCaNB with chloroplast and cytoplasm. Calpain cleavage sites on CaNA suggest differential processing. CaNA and CaNB of these trypanosomatids have the potential to be secreted and could play a role in remote communication. Therefore, this background can be used to develop new drugs for protozoan pathogens that cause neglected disease.Calcineurin (CaN) is present in all eukaryotic cells, including intracellular trypanosomatid parasites such as  Leishmania spp. (Lspp) and Trypanosoma cruzi (Tc), which have unique characteristics. Insect vectors release infective parasite forms during the blood meal in humans, where they face harsh extracellular conditions and quickly invade cells to avoid extracellular immune response . Available from https://www.ncbi.nlm.nih.gov/ (accessed on 01 January 2021)).In the case of the A1 catalytic and CaN regulatory subunits in T. cruzi and Leishmania spp. were obtained using the EMBOSS Cons tool (https://www.ebi.ac.uk/Tools/msa/emboss_cons/ (accessed on 30 May 2020)) )  40]. Th. ThT. crsoftware  using thhttps://prosite.expasy.org/scanprosite/ (accessed on 30 May 2020)) . Moreover, the TcCaNB and LsppCaNB subunits have a hydrophobic groove for the union of the immunophilin–immunosuppressive drug complexes, highlighting the conservation of this region that goes from M119 to L124 (hydrophobic groove), corresponding to HsCaNB-1, being much more hydrophobic in TcCaNB and LsppCaNB than in HsCaNB-1, determined by the grand average of hydropathicity index (GRAVY), which is used to represent the hydrophobicity value of a peptide; positive GRAVY values indicate hydrophobic and negative values mean hydrophilic  in the second position in the N-term in the same way as the HsCaNB-1 subunit. Curiously, despite the fact that the domain structures between HsCaNB-1 and LsppCaNB are similar to each other, CaNB in Leishmania spp. lacks G in its N-terminal region to be myristoylated , while TcCaNB was predicted to be non-myristoylated with a score of −0.146144  was used to analyze possible myristoylation, only 0.146144 .T. cruzi and Leishmania spp. (HsCaNB-1 subunit has a distribution mainly in the cytosolic (cyto) and cyto_nucl compartments, contrasting with TcCaNB and LsppCaNB having a marked distribution at the nuclear level (nucl)  under study using WoLF SPORT suggests a distribution in the cytosolic (cyto), nuclear (nucl), cytosol, and nucleus (cyto_nucl) compartments, and exclusively with a predisposition also at the mitochondrial (mito) level in only the catalytic subunits of nia spp. A, while l (nucl) B.HsCaNAα with 52% cytoplasmic and 5% nuclear; TcCaNA1 with 70% plasmamembrane, 14% cytoplasmic, and 2% nuclear; TcCaNA2 with 27% plasmamembrane, 17% extracellular and cytoplasmic, and 12% nuclear; LsppCaNA1 with 51% plasmamembrane, 21% cytoplasmic, and 10% nuclear; LsppCaNA1_var with 48% plasmamembrane, 18% cytoplasmic, and 9% nuclear; and LsppCaNA2 with 57% plasmamembrane, 25% extracellular, 7% cytoplasmic, and 3% nuclear. In the case of regulatory subunits , in which each one of them presents the CaNB-BD and catalytic domain, have already been identified and characterized [Leishmania, the same analysis has determined that it possesses the four characteristic domains of CaN [TcCaNA2 and LsppCaNA2 subunits  deviate considerably from the range of pI observed for mammalian isoforms. In the case of the isoform α (pI = 5.6–5.8), β1 (pI = 5.3), β2 (pI = 5.6–5.8), however, the isoform γ (pI = 7.1) specific to the testis possesses a higher pI [The domain structures of the catalytic subunits of cterized ,33,37. Os of CaN . In the s of CaN . In addiigher pI . The latigher pI ,62.2+ and CaN-sensitive protease are considered common effectors of Ca2+-induced dysfunctions and degenerations [T. cruzi and Leishmania spp., only TcCaNA1 and LsppCaNA1 possess a cleavage site located between the catalytic domain and the regulatory domain of CaN, which could upregulate CaN phosphatase activity by removing the regulatory domain [TcCaNA2, but not in TcCaNA1, has 4 of the 12 potential sites to be cleaved by calpain [LsppCaNA2 does not present any possibility of being cleaved. The regulation exerted by calpains in the biological cycle of T. cruzi has been studied through the use of inhibitors such as MDL28170, which stops the growth of epimastigote forms [TcCaNA2 in epimastigote proliferation [L. amazonensis [2+, such as calpains and CaN, in basic cellular functions of the life cycle of T. cruzi and Leishmania spp.A transcendent regulatory mechanism for the action of CaNA is cleavage by calpain. Curiously, calpains and calcineurins share the same subcellular distributions and exhibit equally high levels of activity after many of the same types of insults, thus Caerations . Calpainerations , generaterations . From thy domain  or partsy domain , while t calpain , while tte forms , in the feration . Moreoveferation ,70. It azonensis . The invzonensis . Despitezonensis . This re349-Phe350 and Phe356-Val357), which are essential for the interaction between the catalytic subunit and the calcineurin regulatory subunit [T. cruzi and Leishmania, the Val349 residue of the HsCaNAα subunit is not present in their corresponding protozoan orthologs. This makes the hydrophobic profile of the surrounding residues different, which suggests that the degree of differential hydrophobicity of CaNB-BD present in the CaNA of T. cruzi and Leishmania conditions the interaction of the invariant CaNB subunit with the CaNA1 or CaNA2 subunits in the conformation of the heterodimers.It has been described that CaNB-BD in the catalytic subunit αδ of rat brain has four hydrophobic amino acid residues  and potential kinase phosphorylation site (underlined) [LsppCaNA1 and LsppCaNA1_var have an identity of 50% and 53%, respectively, with only four residues with a positive charge; however, they have a greater potential to be targeted for phosphorylation by kinases such as PKA and PKC, either in serine or threonine. It may be a regulatory mechanism of the CaM interaction of the parasite towards the LsppCaNA1 and LsppCaNA1_var subunits, as has been described for other CaM-binding proteins [Unlike the CaM-BD present in erlined) , which ierlined) , the CaMproteins ,76.HsCaNAα, it has been proposed as a pseudosubstrate exerting an action mechanism that blocks the active CaN site [416 ARVFSVLR423; importantly, it interacts with a hydrophobic groove formed at the junction of the CaNA and CaNB subunits [Leishmania spp., as the ASI potential presents three nonpolar residues  of the total of five present in the ASI in the isoforms α and β of human CaNA, and the basic character of the R residue is present in both sequences.In relation to the AID present in CaN site , togetheCaN site . In factsubunits . In LsppT. cruzi and four for Leishmania spp. [Trypanosoma rangeli, a non-virulent trypanosome for mammals, suggests the presence of four EF-hand motifs with a potential role in the growth of epimastigotes, and of these four EF-hand motifs, only three fulfill the expected pattern of amino acid residues involved in coordinating Ca2+ [HsCaNB, the analysis using the Prosite program attributes a lower score to the EH-hand motives towards N-term, while these are higher towards C-term, according to what has already been evidenced [http://prosite.expasy.org/cgi-bin/prosite/ nicedoc.pl?PS00018 (accessed on 30 May 2020)), establishing a conformation pattern of one or two damaged or incomplete EF-hands in the case of TcCaNB [LsppCaNB, respectively, followed by two complete functionals. In the case of CaNB, the domains’ architecture possessed by CaNB of these trypanosomatids is different, with three EF hand motifs for nia spp. ,37. Thesnia spp. ,33,34,35ing Ca2+ . In the videnced . Thus, Cf TcCaNB ,37 and LTcCaNB is also observed in oncomodulin and parvalbumin, proteins that are phylogenetically related to CaNB [2+, while the low affinity sites are regulated by the concentration of Ca2+. Thus, the binding of Ca2+ to the low affinity sites affects the conformation of CaNB and, when associated with CaNA, induces a conformational change in the regulatory domain, which leads to the exposure of the binding domain with CaM. This conformational change, necessary for the partial activation of the enzyme in the absence of CaM, allows it to become fully active when associated with CaM [The conformation pattern of the EF hand motifs  of  to CaNB . Therefowith CaM ,82.2+ to L (basic) of HsCaNB, the individual role of each EF hand motif was determined (by eliminating the Ca2+ binding capacity in each of them), showing that those mutations in the EF-hand motifs 1 and 2 (towards N-term) suffered variations in their electrophoretic mobility depending on the presence or absence of Ca2+. Meanwhile, in those with mutations in EF-hand 3 and 4 motifs (towards C-term), no differences were observed in the electrophoretic pattern, suggesting that Ca2+ binding in EF-hand motifs 1 and 2 varies according to their concentration, dynamically modulating the enzymatic function, while EF-hand 3 and 4 would always be saturated with Ca2+, fulfilling a structural role [45 Ca2+ exchange is faster in EF-hand 3 than EF-hand 4 mutated, indicating that these sites are not equivalent and that the effects on heterodimer formation are greater in the EF-hand 3; this could explain the differences observed in the two functional EF-hands of TcCaNB, establishing a structural role for EF-hand 3 and a conformational role for EF-hand 4, and for LsppCaNB, a structural role for EF-hand 3 and 4 and a conformational role for EF-hand 1 and 2.Through the generation of mutants in the E (acid) residue at position 12 of each loop that binds Caral role . Moreove2+-dependent mechanism [2+ induces an association between CaN and NFAT, which results in the colocalization of both molecules in the nucleus [With respect to the subcellular distribution of CaN, in mammals, it associates predominantly in the cytoplasm and in the synaptosomal cytosol . It has echanism . Regardi nucleus . It is i nucleus . These r nucleus , while t nucleus . TherefoPlasmodium falciparum including HSP90, actin-1, and phosphoglycerate kinase [T. cruzi, CaN would act during the process of cell invasion on protein targets of high molecular mass [Leishmania spp., it has not been addressed.Studies in yeast have shown that CaN dephosphorylates the transcription factor Crz1p in vitro and that its location is displaced from the cytosol towards the nucleus subsequent to the in vivo activation of CaN, demonstrating that CaN regulates the phosphorylation and localization of Crz1p, and identifies Crz1p as the first CaN substrate protein . Furthere kinase ; in T. clar mass , while iLeishmania spp. and in T. cruzi [T. brucei, there is information on the cellular localization by microscopy of the CaN subunits: for TbCaNA1 (Tb927.9.1540) in cytoplasm and flagellar cytoplasm; for TbCaNA2 (Tb927.10.6460) in cytoplasm, flagellar cytoplasm, and nuclear lumen; and for TbCaNB (ID Tb927.10.370) in endocytic compartmen and cytoplasm (see TrypTag.org database). In the case of Leishmania donovani, CaN has a cytosolic localization in promastigote forms that depends exclusively on Ca2+ and CaM for its activity, which was biochemically evidenced [T. cruzi, the TcCaNA1 subunit presents an evidently nuclear cellular localization in epimastigotes and amastigotes [TcCaNA2 presents a cytosolic localization in epimastigotes [TcCaNB, a predominant localization was observed to be cytosolic with some accumulation in the kinetoplast (data not shown). These analyses support a probable co-distribution of the TcCaNA2 and TcCaNB subunits.Regarding the characterization of some CaN subunits in kinetoplastids, this has been described in T. cruzi , and in videnced . In T. cstigotes , and in T. cruzi and Leishmania spp., in the CELLO2GO tools [T. cruzi and Leishmania spp. have a marked predisposition for plasma membrane over cytoplasmic localization and, in the case of CaNA2, a strong extracellular component. These characteristics suggest that these phosphatases may be secreted and behave as virulence factors acting synergistically or independently in infected cells [TcCaNB, the analyses suggest that a localization in the chloroplast may be due to its proximity to the calcineurin B-like proteins present in plants, which have substitutions in their first EF-hand motif that seem to be unable to bind Ca2+ [Through the in silico analysis of the amino acid sequences of the CaN subunits in GO tools  and WoLFGO tools , we souged cells ,92,93. Iind Ca2+  with theT. cruzi and Leishmania, studies indicate that they produce ectosomes, exosomes, and other soluble proteins not associated with vesicles from which they are released to deliver cargo on the host cells [Leishmania spp. suggest that the exosome pathway is an unconventional protein secretion mechanism as most of the exosome proteins do not contain a predicted signal peptide [TcCaNA1, TcCaNB, and LsppCaNA2 (NN-score > 0.6); in the case of LsppCaNA1 and HsCaNB, they have a close score, which argues in favor of the potential of these molecules to modulate the host cell, as in the case of some phosphatases described in T. cruzi [T. cruzi secretome has revealed the presence of Ca2+ binding proteins such as calreticulin (gi|322823951) involved in host–parasite interaction, a calcium-binding protein (gi|487896), calmodulin (gi|10386) involved in cell signaling, and a calpain-type cysteine peptidase (gi|322830271) whose role is associated with proteolysis [Studies have shown that several extracellular proteins can be exported without possessing a classical N-terminal signal peptide, such as FGF-1, FGF-2, IL-1, and galectins . In the st cells ,97,98,99 peptide . The anaT. cruzi ; particuteolysis .Leishmania and tumor progression [HsCaNB has acquired interest as it has been attributed anti-tumor roles, categorized as immunity-mediated killing (extracellular role) and direct pro-apoptotic killing (intracellular role) [HsCaNB is a ligand of the αM integrin, which corresponds to a subunit of the heterodimeric integrin αMβ2, a receptor expressed mainly on the surface of innate immunity cells , suggesting that the levels of HsCaNB in the serum are important for the maintenance of the innate immune response and the immune surveillance of cancer, through the activation of the monocyte–macrophage axis [HsCaNB uptake depends on the TLR4/MD2/CD14 receptor complex, indicating a second membrane receptor (TLR4) plays a critical role in innate immunity and its connection with adaptive immunity [HsCaNB (rhCNB) has been implicated in the inhibition of the proliferation of gastric cancer cells and hepatomas, through the induction of apoptosis and arrest of the cell cycle, as rhCNB promotes the expression of p53 and decreases the expression of cyclin B1 and cyclin-dependent kinase 1 (Cdk1), contributing to arrest in G2/M [TcCaNB owing to their potential secretion) could play a role as mediators of the host immune response  during parasite infection and tumor formation.Several data have shown similar host responses against gression ,103. Botgression ,105. Howgression . In thisgression  shows a gression  and tumoar role) . In theiage axis . It was immunity . Recentl in G2/M . Through in G2/M . These sTcCaNB and LmCaNB based on the parameters used, the predictions establish contact between the CaNB subunits and the CaNA subunits, and between the CaNB subunits and peptidylpropyl isomerases. In particular, in the case of TcCaNB, the predicted functional partners XP_810491.1  and XP_808861.1  are putative sequences that have a 98–99% identity with TcCaNA1 [TcCaNB and TcCaNA2, as evidenced in vitro by far-Wester blotting [TcCyP19–trialisin complex show a greater capacity to invade the host cell through activation the parasite CaN pathway [TcCaNB is mainly in the vicinity of the flagellar pocket (unpublished results) in infective and replicative forms, strongly suggesting that these T. cruzi proteins are secreted by events restricted to the flagellar pocket [With regard to the protein–protein interactions determined for  TcCaNA1 , while Sblotting . Neverthblotting , behavinblotting , and tho pathway . Curiousr pocket  such as r pocket .T. cruzi and Leishmania.Finally, the search for a new potential target as a parasitic CaN should have the objective of developing drugs that act specifically on pathogens while being non-immunosuppressive . AnotherLeishmania (LsppCaNA1 and LsppCaNA1_var). In the case of regulatory subunits, the domain structure is different, with LsppCaNB being more similar to the human regulatory subunit (HsCaNB-1) than to TcCaNB, although the binding affinity for Ca2+ is conserved between TcCaNB and LsppCaNB. On the other hand, although the coupling sites to the immunophilin–immunosuppressive drug complexes are present in TcCaNB and LsppCaNB, the myristoylation potential is only found in HsCaNB-1.In this work, it was possible to establish that the domain structure is diverse among the catalytic subunits of CaN of intracellular trypanosomatids, thus establishing potential different post-translational regulation mechanisms observed in the analysis of cleavage by calpains, or by the phosphorylation patterns in the regulatory domains of the catalytic subunits, particularly in the A1 subunits in T. cruzi and Leishmania spp. preferentially (more than 50%) present a localization in the plasma membrane (with the exception of TcCaNA2a with only 27.2%), different from HsCaNAα, which is predominantly cytoplasmic (51.7%). In the case of regulatory subunits, the distribution was more heterogeneous, being more associated with chloroplast and cytoplasmic for LsppCaNB  and preferentially in cytoplasm (48.6%) for TcCaNB, when comparing the nuclear/cytoplasmic localization of HsCaNB-1 .Regarding the analysis of the subcellular distribution, the catalytic subunits of TcCaNB and LsppCaNB potential interactors. Concerning the potential secretion, TcCaNA1, TcCaNB, and LsppCaNA2 can be secreted by the non-classical pathway, suggesting new extracellular roles for these protein phosphatases.Besides the catalytic subunits, the interaction with molecules with peptidylprolyl isomerase activity, which is typical of cyclophilins and FKBPs, is confirmed in the analysis of On the basis of these in silico data, differential CaN regulation mechanisms are established between these protozoa and its human counterpart, complementing the knowledge of this phosphatase, promoting the development of new potential pharmacological targets to combat neglected diseases caused by these intracellular trypanosomatids.


Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR diagnosis categorize retinal ophthalmoscopy images into training and validation data sets according to the 80/20 rule, and they use the synthetic minority oversampling technique (SMOTE) in data processing  to increase the number of training samples. Oversampling training may lead to overfitting of the training model. Therefore, untrained or unverified images can yield erroneous predictions. Although the accuracy of prediction results is 90%–99%, this overfitting of training data may distort training module variables.This study uses a 2-stage training method to solve the overfitting problem. In the training phase, to build the model, the Learning module 1 used to identify the DR and no-DR. The Learning module 2 on SMOTE synthetic datasets to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR classification. These two modules also used early stopping and data dividing methods to reduce overfitting by oversampling. In the test phase, we use the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets to evaluate the performance of the training network. The prediction accuracy achieved to 85.38%, 84.27%, 85.75%, 86.73%, and 92.5%.Based on the experiment, a general deep learning model for detecting DR was developed, and it could be used with all DR databases. We provided a simple method of addressing the imbalance of DR databases, and this method can be used with other medical images. Patients with diabetes visit the hospital when their eyesight worsens. They often have proliferative diabetic retinopathy (DR) or vitreous hemorrhage. Currently, doctors can detect symptoms early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency by using deep learning techniques to select treatments and support personnel workflow .The determination of retinopathy severity requires extensive professional knowledge. Depending on physician experience, interpretations of the same data set can vary, causing errors. Therefore, images must be quantified. The comparison of preoperative and postoperative data can help physicians judge whether an operation is successful. In addition, diagnoses can be confirmed using machine learning and deep learning and required treatments can be clearly identified. These techniques can help physicians make accurate diagnoses and identify lesions , 3.The results obtained from optical coherence tomography, ophthalmoscopy, and the automatic classification of multicategorical anomalies based on deep learning are similar to those obtained from clinical classification, the accuracy of these imaging methods is only 70–85% –7. A deeAbnormal samples in medical images are less common than normal samples and often valuable; therefore, studying unbalanced data is crucial. Unbalanced data can be addressed using oversampling ) , 11, undThe aforementioned methods for processing unbalanced data can produce high diagnostic rates. When the parameters of the deep learning model are sufficiently complex, the training model will record the features of all pictures in the training data set, so untrained samples usually lead to recognition errors. Therefore, some scholars pointed out that oversampling may lead to overfitting of the model , 20.There are methods to avoid overfitting, such as: (a) collecting more data: more data can help models training better; (b) stopping training early: when the validation loss rises, you can stop training immediately. Because after that, more training may make the model worse; (c) normalizing L1 and L2: prevent the model from being affected by the parameters with higher weight coefficients, which will lead to overfitting; (d) dividing data set: divide data sets into three groups, keep the holdout data as the test set to estimate the generalization performance of model. In this study, we apply the stopping training early and dividing the dataset into three parts to avoid the overfitting.To prove that oversampling will lead to overfitting of the model. We apply the SMOTE algorithm to synthesize several types of images in the open training database of the Kaggle competition, and we trained and validated datasets at 80:20 training-to-validation set ratio; then, we fine-tuned a pretrained AlexNet , 24 convThis study used the classic method of preventing overfitting by dividing the data sets into three groups  and used a two-stage training approach to mitigate overfitting caused by SMOTE. Subsequently, we provide a complete description of the DR databases and experimental data used to validate our proposed method.We used six public datasets for model training and testing. The EyePACS  dataset EyePACS is a free platform that includes retinopathy images. A clinician rated DR in each image on a scale of 0–4, where 0 indicates no DR, 1 indicates mild DR, 2 indicates moderate DR, 3 indicates severe DR, and 4 indicates PDR. In our study, images with classifications of 0, 1, 2, 3, and 4 were represented by 65,343, 6205, 13,153, 2087, and 1914 training samples, respectively.The DIARETDB0 data set consisted of fundus images. We tested their algorithms by using 130 image communities capture using a 50° field of view. Of these images, 110 presented signs of DR, and 20 were normal.The DIARETDB1 database were captured using a 50° field of view. Of these images, 5 were normal and 84 presented signs of DR.The eOphtha is a free database that provides color fundus images, of which 47 had exudates and 35 were normal.The MESSIDOR  data set includes 1251 digital color images of the posterior pole obtained using a Topcon TRC NW6 nonmydriatic retina camera with three color charge-coupled devices. Images were captured at resolutions of 1440 × 960, 2240 × 1488, or 2304 × 1536 pixels using an 8-bit color plane.The DRIVE   data setWorkstation: Supermicro GPX XS8-24S1-4GPU; central processing unit: Intel Xeon Platinum 8165 processor *2; random-access memory: 128-GB DDR4 ECC 2666; graphics processing unit (GPU), 16-GB NVIDIA TESLA P100 *3.Operating system and software: Windows 10 (64 bit), Matlab2019a Deep Learning Toolbox, Compute Unified Device Architecture (CUDA) Toolkit 10.1, CUDA Deep Neural Network v7.6.0 .The deep learning machine presented in Fig. We divided all the data sets into two parts, the training phase used the EyePACS dataset; and the test phase used the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets.In training phase, the main cause was to build the Diabetic Retinopathy detection model, the Deep Learning module 1 used the preprocessed EyePACS dataset and pre-trained NASNET-Large to identify the DR and no-DR. The Deep Learning module 2 apply the SOMTE synthetic samples to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR. When validation loss rises, these 2 modules stop training immediately to perform the hyperparameters tuning then restart the training to avoid the model worse.In test phase, the test data sets input the Deep Learning module 1 to identify DR and no-DR categories, then the DR images use Deep Learning module 2 for the classification to mild-NPDR, moderate NPDR, severe NPDR and proliferative DR. The overall system flowchart for classifying DR categories is presented in Fig. Normalization of fundus images: The resolutions of the original fundus images were inconsistent . However, if images were adjusted to a uniform 331 × 331-pixel resolution, the eyeball sphere would be deformed, resulting in low-resolution images in deep learning training. Therefore, we first converted color images into grayscale through binarization. Subsequently, connected-component labeling was performed to mark all connected areas of the image and identify the largest connected area, which was focused on the eyeball in the fundus images. Finally, the original color images were cropped according to the range of the maximum connected region to obtain images cropped at the edges of the eye.Pretrained deep neural network selection: Transfer learning was used to establish a diagnostic model for medical imaging analysis, and we selected the pretrained NASNet-Large CNN to classify our images. According to MATLAB 2019a documentation [entation , transfeUnbalanced data processing: Traditional SMOTE data processing involves the rotation, scaling, and translation of training images to increase the number of training samples. SMOTE may cause variable importance distortion in the training module. Therefore, SMOTE was not used in the first step of deep learning training to address data imbalance. We first used a decision tree and NASNet-Large transfer learning to initially classify DR into no-DR  and DR  categories. Second, the DR category was classified into four categories based on NPDR and PDR. Finally, the five-stage classification of DR severity was used in combination with the two aforementioned diagnostic models.In hyperparameter adjustment, this research applies 2 phase experiment. First, execute the 56 epochs to separate the performance of SDGM and ADAM under the learning rate by 0.0001 and 0.001. Figure This study used the classic method of preventing overfitting by dividing the data sets into three groups  and used a two-stage training approach to mitigate overfitting caused by SMOTE.We used the NASNet-Large deep CNN to pretrain the model, which contained EyePACS images with no DR  and DR . The Deep Learning module 1 achieved an accuracy of 0.85 in terms of no DR and DR classification. The combined Deep Learning module 1 and Deep Learning module 2 trained network had an accuracy of 0.83 in classifying DR into five categories of severity . Finally, we used the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE data sets to evaluate network performance.Deep learning module 1 (DLM1): First, we split the EyePACS dataset  into training arrays  and validation arrays  with a batch size of 32 images. Each epoch underwent approximately 2217 iterations . Training was run for 36 epochs, and a maximum of 79,812 iterations was obtained in 33,415 min of training time. The primary outcome measures of accuracy, sensitivity, specificity, and precision for the classification scores were 85.00%, 84.89%, 85.29%, 94.17%, respectively. The final trained network DLM1 achieved 85.0% accuracy for the trained and validated set and the confusion matrix in Fig. Deep learning module 2 (DLM2): Then, we used SMOTE to train the model by using the EyePACS data set for four disease severity classifications. The DR level 1 to 4  images are rotated, zoomed, and translated to increase the number of images through the SMOTE method. At the beginning of training, we divide all the data into three subsets: training set , validation set  and test set . The final trained network had an accuracy over test data is 84.19%. The confusion matrix in a 4-class classification for the test set and EyePACs database for DLM2 model is shown in Fig. Deep learning module (DLM): In order to complete the final trained network, we combine the DLM1 and DLM2 and had an accuracy of 83.93% for DR 0–4 classification.After internal validation, the general deep learning module for detecting DR was validated externally by using 130 images from the DIARETDB0 data set, 89 images from the DIARETDB1 data set, 463 images from the eOphtha data set, 1251 images from the MESSIDOR data set, and 40 images from the DRIVER data set that were not included in the EyePACS data set. To enable accurate validation, external validation was performed without alterations to the general deep learning model for DR detection. The results are summarized in Table We used the NASNet-Large deep CNN to pretrain the model, which contained EyePACS images with no DR , mild NPDR (6205 sheets), moderate NPDR , severe NPDR (2087 sheets) and proliferative DR (1914 sheets). In order to balance the number of DR images to achieve effective feature deep learning, the DR level 1 to 4  images are rotated, zoomed, and translated to increase the number of images through the SMOTE method. The final trained network had an accuracy of 89.79% in classifying DR into five categories of severity.To enable accurate validation, external testing was performed without alterations to the general deep learning model for DR detection. The results are summarized in Table We adopted a 2-stage training method to classify the data into normal and abnormal in deep learning module 1, and then used the SMOTE in deep learning module 2 to solve the problem of imbalance training data. This study used a 2-stage training method initially to reduce oversampling, early stopping and data dividing methods to avoid overfitting. In a conventional study using the 1-stage training method, the SMOTE will generate 238,013 synthetic samples under an unbalanced data set. This research proposes a 2-stage training method to generate 29,253 images to meet the requirements of a balanced data set. Comparing the number of the above two synthetic samples, the method proposed reduces the number of images by nearly 87.7% and reduces the possibility of overfitting.According to our experiments, 2-stages training method is better than the 1-stage training method and the diagnostic model’s prediction accuracy for unseen images (test set) was approximately 85.38%–86.73% similar to the 2015 the team (Min-Pooling) that won the championship in the Kaggle Diabetic Retinopathy Detection competition in 2015. Therefore, this diagnostic model can be used for DR detection with images provided by different hospitals or research institutions. It can be used as a general deep learning model for detecting DR.EyePACS provided DR images for this experiment. Images in the dataset were obtained using different camera makes and models, which may affect the visual appearance of the left and right lenses. Furthermore, image and label noise, such as artifacts, blurred focus, underexposure, or overexposure may be present. Therefore, image processing techniques must be used to extract useful features from these images for further analysis.The experiment revealed that the failure rate of image cropping at the edge of the eyeball is high. After several tests, a GRAYTHRESH function  with a gIncreased convergence can be achieved with a large minibatch size. In addition to the effect on computational throughput, minibatch size affects training accuracy for resource use. To investigate this, we trained the NASNet-Large CNN with the EyePACS data set for 54 epochs, and three NVIDIA TESLA P100 16-GB GPUs were used during deep learning to explore the effect of different minibatch sizes on the accuracy rate. Table In this article, for the multi-category diagnosis of diabetic retinopathy, we use a two-stage training model architecture. In the first stage, we do not use artificial methods to increase the number of data sets, and integrate the original images of 2, 3, and 4 categories into 1 category to obtain a larger number of 1 category. The no-DR and DR diagnostic models can avoid over-fitting problems caused by over-sampling. Finally, the sensitivity and specificity of the tested model rarely ignore true positives, and rarely identify other things that are not the target of the test as positive. This approach of this two-stage training model significantly alleviates the problem of category imbalance and improves the generalization performance of the deep learning module.In this experiment, a general deep learning model for detecting DR was developed for application in all DR databases. Our research provides a straightforward method of addressing imbalance in DR databases that can be applied to other medical images in the future.


Prevotella, Bacteroides, and Escherichia, respectively, whereas the fourth one had no dominant genus. By assessing the association between the gut microbiota and 20 variables belonging to six categories, geography, demography, diet, urbanization, lifestyle, and sampling month, we revealed that geography explained the largest microbiota variation, and clarified the distinct patterns in the associations with staple food type, ethnicity, and urban/rural residence. Specifically, the gut microbiota of Han Chinese and ethnic minority groups from the same sites was more alike than that of the same ethnic minority groups from different sites. Individuals consuming wheat as staple food were predicted to have more microbial genes involving in glucan 1,3-beta-glucosidase and S-adenosyl-l-methionine biosynthesis than those who consumed rice, based on functional prediction. Besides, an appreciable effect of urbanization on decreased intra-individual diversity, increased inter-individual diversity, and increased proportion of the Bacteroides enterotype was observed. Collectively, our study provided a nationwide gut microbiota baseline of the Chinese population and knowledge on important covariates, which are fundamental to translational microbiota research.The gut microbiota could affect human health and disease. Although disease-associated microbiota alteration has been extensively investigated in the Chinese population, a nationwide Chinese gut microbiota baseline is still lacking. Here we performed 16 S rRNA gene sequencing on fecal samples from 2678 healthy Chinese individuals, who belonged to eight ethnic groups and resided in 63 counties/cities of 28 provinces. We identified four enterotypes, three of which were enriched for  The gut microbiota provides substantial benefits to our health by forming a barrier against pathogens, producing bioactive metabolites, and regulating immunological functions. The homeostasis of the gut ecosystem is maintained by some core species that are generally shared among different individuals, and the gut microbiota in healthy adults is relatively stable in the absence of strong influencing factors 3.The human intestine harbors a special diverse microbial ecosystem, with an estimated 150–400 bacterial species reside in our gut4. Plenty of microbial components have been revealed to involve in a series of pathologies by extensive disease-targeted microbiota researches, and can thus, in theory, serve as biomarkers. For example, fecal microbial markers for screening colorectal cancer have been widely studied5. However, the translation of microbiota research into clinical practice is still limited by multiple challenges, especially the difficulty in the precise classification of “healthy” microbiota, which requires comprehensive knowledge of the microbiota variation and covariates of an average, healthy population. A study based on 7009 individuals from 14 geographic districts in one province of China demonstrated that microbiota-based metabolic disease models developed in one location could not be extrapolated to other locations, and the efficiency of interpolated models decreased as geographic scale increased6. This emphasized the influence of geography on gut microbiota composition and disease model application, whereas the microbiota variation on a larger geographic scale across China is yet to be explored.The imbalance of the gut microbiota  is associated with many diseases, e.g., inflammatory bowel disease, obesity, allergies, and autoimmune diseases8. For example, stratification of the gut microbiota (termed enterotypes) has been associated with diet, especially the intake of fibers and carbohydrates9; urbanization has been associated with increased inter-individual variation and loss of species with high potential for fiber degradation10; taxa that were differentially abundant across ethnicities have been proposed to be associated with chronic diseases12. To date, most population-level studies investigating gut microbiota covariates have focused on the western population (Europe and the USA) and a few in Israel, Japan, and China13, but rarely in Africa, South America, and other regions of Asia.Besides geography, many gut microbiota covariates have been uncovered, including diet, lifestyle, ethnicity, socioeconomic status, medication, and genetics15.There are various food styles in China, and people living in different regions show great varieties in their diet. Moreover, there are 56 ethnic groups in China, which have distinct characteristics in diet, lifestyle, custom, and culture. Therefore, the diversified gut microbiota is expected in the Chinese population. Meanwhile, as a result of an unprecedented speed and scale of urbanization, Chinese is undergoing rapid change in lifestyle, and the dietary habit is shifting towards a western-style diet. Specifically, more high-fat and high-protein foods are consumed while fewer grains are taken, which might significantly change the gut microbiota as observed in other developing countries. Although a couple of studies have been conducted to investigate the gut microbiota characteristics in China, these studies either focused on limited regions or recruited a small number of participants, and a nationwide gut microbiota survey is still missingTo characterize the gut microbiota diversity in the Chinese population and investigate microbiota-associated variables, we collected feces of 2678 individuals without apparent diseases , which underwent 16 S ribosomal RNA (rRNA) gene sequencing (V3–4 region). A questionnaire including information on demography, diet, and lifestyle was carried out, which enables an in-depth analysis of factors associated with the Chinese gut microbiota.We recruited 2678 healthy volunteers  from 63 counties/cities of 28 provinces, including 2167 Han Chinese (1755 with age over 3 and 412 with age under 3), 487 individuals from seven ethnic minority groups , and 24 individuals without ethnicity information Fig. . Fecal s16. In our data, we observed a strong positive correlation between the alpha diversity and age in children under age 3 , R2 = 0.35, p < 2.2e-16), but not in other age groups  for receiver operating characteristic (ROC) curve: 0.99, Supplementary Fig. Prevotella enterotype , Bacteroides enterotype , Escherichia enterotype , and mixture enterotype   being the third enterotype in most previous studies19, E3 was distinguished by an overrepresentation of Escherichia , which has rarely been reported20. Besides, E4 showed no dominant genus but a mixture of a few relatively abundant genera, including Bifidobacterium and Blautia etc.Firmicutes, Bacteroidetes, Proteobatcteria, and Actinobacteria were the four most abundant bacterial phyla in all samples Fig. . A total40) Fig. . E1 and R2 > 0.01, p < 0.0001, Fig. Covariates associated with the alpha diversity of gut microbiota were first investigated with simple linear regressions. Faith’s PD and Observed ZOTUs were significantly correlated with geographical zone ) was significantly correlated with 17 covariates as evaluated by envfit22. Consumption of the two grains also showed similar geographic distribution , wheat (white flour of common wheat), and rice & wheat. Owing to the requirements in temperature, precipitation, and sunshine duration for different grain crops, wheat was mainly grown in northern China, whereas rice was cultivated more widelyion Fig. . Intrigup.adj < 0.05, Fig. Bifidobacterium and Catenibacterium were enriched in individuals consuming wheat and wheat & rice when comparing to individuals consuming rice. The abundances of these two genera decreased gradiently in the three groups, indicating that there might be a dose-effect associated with wheat intake. The association between Bifidobacterium and wheat intake is consistent with previous observations that the abundance of Bifidobacterium was decreased when taking diets with low wheat content, e.g., gluten-free diet, low-gluten diet, and low FODMAP  diet28. Twelve genera were enriched in individuals consuming rice and rice & wheat when comparing to individuals consuming wheat, of which Bacteroides, Parabacteroides, a butyrate producer Clostridium XIVa and an opportunistic pathogen Fusobacterium were the most significant ones . However, no difference was observed in the enterotype composition among the three groups.Bacterial genera differing between each two groups were identified using DESeq2 analysis with adjustment for age and gender (29. Considering that common wheat flour contains more dietary fiber than white rice (2–3% vs. 0.7–2% of dry matter) and the fiber components differ significantly between the two grains31, we specifically focused on 69 ECs belonging to glycosidase (EC 3.2.1). The abundances of 19 glycosidases differed between the Wheat and Rice group  and l-arginine etc. Of note, SAM is widely adopted as a therapy for liver disease, depression, and osteoarthritis33. Besides, the Wheat group was associated with a higher potential of housekeeping functions including cell structure biosynthesis and nucleic acid processing. These findings indicate that the staple food type and possibly related dietary habits may alter the metabolism capacity of gut microbiota.To explore the metabolism capacity of gut microbiota affected by distinct staple foods, enzyme commission (EC) numbers and MetaCyc pathways were inferred with PICRUSt2R2 = 4.00%, p < 0.001, permutational multivariate analysis of variance (PERMANOVA) based on JSD, supplementary Fig. p.adj < 0.05, Fig. p < 0.05 except for Tibetan vs. Han from the same site, PERMANOVA based on JSD, Fig. p < 0.0001 for Uygur, Hui, and Mongolian), and correspondingly, the sampling site explained larger variance in the gut microbiota than the ethnic group   Fig. . These oCatenibacterium contributed the most power to the classification, and the representative genera of enterotypes, Bacteroides (E2), Escherichia (E3), and Prevotella (E1), ranked 5th, 8th, and 9th of the contributing genera, respectively. Correspondingly, the enterotype composition differed among ethnic groups . We further applied DESeq2 models to detect ethnicity-specific genera by comparing one ethnic group to the rest, with adjustment for the confounding factor sampling site; for Uygur, Hui, Mongolian, and Tibetan, we also compared each of them to their accompanying Han samples from the same sampling sites. Differential genera (p.adj < 0.05) detected by both models included a lower level of Clostridium XVIII in Uygur, lower levels of Prevotella, Fecalibacterium, and Alistipes, whereas a higher level of Romboutsia in Hui, and higher levels of Holdemanella and Enterococcus whereas a lower level of Escherichia in Mongolian . Of note, the intra-group microbiota dissimilarity evaluated by JSD was higher in the urban residents  showed more significant differences between the two groups , including Erysipelotrichaceae incertae sedis and Parasutterella that were higher in the urban population, as well as Alloprevotella and Catenibacterium that were higher in the rural population.The microbiota communities were compared using DESeq2 analysis to determine genera segregating urban and rural populations  on the gut microbiota, and to what extent were these effects homogenized by, for example, co-residing with Han Chinese. Furthermore, our finding highlighted that urbanization was associated with decreased intra-individual diversity and increased inter-individual diversity of the gut microbiota. Previous studies based on one or two provinces of China have shown a similar pattern in the bacterial, fungal, and viral components38, whereas our study based on 28 provinces further validated the vast effect of urbanization on the gut microbiota across China. In addition, although sampling month appeared to be a notable signal, we did not find any specific seasonal pattern in alpha or beta diversity. In contrast, the seasonal rhythm of gut microbiota and its CAZYome diversity has been shown in hunter-gatherers39. We speculate that the lack of association between the gut microbiota and season in this cohort is because lifestyle, especially diet, is much less affected by season in the modernized population.We have conducted a study on Chinese gut microbiota with a large cohort covering by far the greatest diversity of the healthy population. We found that a series of factors belonging to five categories, i.e., geography, demography, diet, urbanization, and sampling month, explained a substantial proportion of the gut microbiota variation, although the effect sizes of some factors  were likely under-estimated owing to the uneven number of samples in some subgroups. First, geographic factors showed the strongest signals, including sampling site, geographical zone, altitude etc. Specifically, the gut microbiota of Han Chinese and ethnic minority groups from the same sampling sites was more alike than that of the same ethnic minority groups from different sampling sites, underscoring the importance of considering the geographic location in case-control studies. Although gut microbiota has been widely reported to vary across geography40. The Chinese population mainly consumes two distinct types of staple food, wheat (products made from white flour of common wheat) and rice (boiled white rice). In this study, the wheat-consuming population was predicted to have a remarkably higher level of microbial genes encoding glucan 1,3-beta-glucosidase, the substrate of which only exists in wheat but not rice. This confirmed the validity of not only differences in the amount of wheat intake acquired by questionnaires, but also functional prediction based on 16 S rRNA gene profiles. Further, the predicted increase in the microbial biosynthesis capacity of l-methionine and its major downstream product SAM in the Wheat group is of special interest, owing to the broad involvement of SAM in cognitive and metabolic health32. Notably, the alteration of microbial l-methionine biosynthesis as well as the archaeal conversion of SAM has been shown in an intervention study with gliadin (one of the protein fractions of wheat) in mice, which was based on urinary metabolome with ultra performance liquid chromatography-mass spectrometry41. Therefore, the possible effect of staple food on SAM biosynthesis may be narrowed down to the effect of the protein component of wheat, gluten, which could trigger celiac disease in 0.06% of the Chinese population42. Thus, we speculate that gluten may modulate the health of gluten-tolerant individuals by regulating the gut microbiota, the validity, and mechanism of which warrant further investigation. In addition, since wheat is more popular in north China than in south China, the observed effect of staple food type on gut microbiota could be confounded by geographic locations.It is well acknowledged that diet alters gut microbial composition and metabolism, but the study on the long-term effect of staple food type on gut microbiome at the population level is still missingBacteroides, Prevotella, and Escherichia drove the diversification of the gut microbiota of the Chinese population , dietary information ), and lifestyle . Specifically, the ethnic information was confirmed through participants’ Resident Identity cards. BMI z scores were calculated for individuals younger than 18 years old using the Zanthro function of STATA package v15.0, based on the World Health Organization Child Growth Standards 2007. Urbanization information  was collected from the National Bureau of Statistics of China and China Statistical Yearbook (2017 and 2018). Geographic information  and sampling month were collected in the meantime. Geographic distances between sampling sites were represented by Vincenty distances, which were computed using the geosphere R package v1.5-1049.The 2678 Chinese participants had no self-reported gastrointestinal tract disorder or any other acute/chronic/recurrent medical conditions , and they had not taken any antibiotics for at least 3 months prior to participation. All recruited rural or pastoral residents lived a typical farming or pastoral lifestyle away from the metropolitan areas. Fecal samples were collected following a standardized procedure: the participants were informed of detailed instructions, collected samples by themselves, and stored samples in home freezers or iceboxes; samples were transported to the freezer at each sampling site within a day, and further to the research laboratory with cold-chain within 3 days; samples were then well homogenized, aliquoted, and stored at −80 °C until further analyses. The metadata  following the manufacturer’s instructions. The V3–4 region of 16 S rRNA gene was amplified by the primers 314 F (CCTAYGGGRBGCASCAG) and 806 R (GGACTACNNGGGTATCTAAT) jointed with a seven-base-pair barcode. The PCR product was purified by the QIAquick Gel Extraction Kit , and sequenced on the Illumina Miseq platform with the Miseq Reagent Kit V3 .50. The reads were de-multiplexed, and barcode and primer sequences were removed with Cutadapt v2.1151. All sequences were subjected to quality filtering with a cutoff of maxee 1.0 and de-replicated, and unique sequences with more than seven replicates were clustered to ZOTUs using USEARCH. Taxonomy of ZOTU representative sequences was assigned using the SINTAX algorithm of USEARCH and the adjusted Ribosomal Database Project (RDP) training set v16  with a cutoff of 0.8. The phylogenetic tree was constructed by inserting ZOTU representative sequences into the 99% Greengenes 13_8 reference tree using the SEPP algorithm52 with QIIME2 v2018.1053. The functional potential was predicted based on ZOTUs using PICRUSt229, generating EC number and MetaCyc pathway abundances.Paired-end sequencing reads were merged using USEARCH v11.0.66754. The microbiota variation explained by environmental variables was evaluated with envfit function of the vegan R package v2.5–621, and the differential clustering of microbial communities was assessed using PERMANOVA with adonis function of vegan. The Correlation between geographic distances and microbial JSD was estimated by Mantel statistic based on Spearman’s rank correlation, using mantel function of vegan.For analyzing the microbiota alpha and beta diversity, the ZOTU table was rarefied to 10,000 reads, and observed ZOTUs, Faith’s PD, Shannon index, Bray–Curtis distance, and unweighted UniFrac distance were estimated using QIIME2, whereas JSD was estimated using the phyloseq R package v1.32.014. Samples were clustered with the pam function of the cluster R package v2.1.055. The optimal number of clusters was four according to the Calinski–Harabasz Index, giving an average silhouette coefficient of 0.14. The clustering was visualized on PCoA using the ade4 R package v1.7-1555. To identify the driving genera of each enterotype, random forest analysis with ten-time fivefold cross-validation was performed using the randomForest R package v4.6-1456. The representative genus Escherichia/Shigella was referred to as Escherichia, considering that all subjects had no symptoms of Shigella infection at the time of sampling.For the rest of the analyses, total sum scaling was applied to normalize the microbiome data unless otherwise specified. Enterotype analysis was done based on the method described by Arumugam et al.57. To even out the number of individuals in each ethnic group, the Han Chinese were randomly downsampled to 6% of the full data set 1000 times. Accordingly, the Mean Decrease in Gini, AUC, sensitivity, specificity, and precision were calculated as the average value from 1000 random forest models.Random forest models with ten times fivefold cross-validation was applied to identify genera distinguishing the eight ethnic groups using the scikit-learn Python package v0.23.158, and visualized using Cytoscape v3.5.159. Differential genera were detected using the DESeq2 R package v1.29.1460, with adjustment for age and gender unless otherwise specified. Genera with mean relative abundance >0.1% and presence in >50% of the samples in at least one group were used for the above analyses unless otherwise specified.To examine the microbiota co-occurrence network, correlation analysis was performed on genera  using SparCC61 with a significance level of 0.05 .Two-tailed Mann–Whitney test and Fisher’s exact test were used to compare continuous variables and categorical variables, respectively. Multiple comparisons were corrected using the Benjamini–Hochberg false discovery rate algorithmFurther information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Background/objectives: Amyotrophic lateral sclerosis (ALS) is a devastating and still untreatable motor neuron disease. The causes of ALS are unknown, but nutritional factors may impact the rate of disease progression. We aimed to ascertain the influence of coffee and tea consumption on ALS progression rate.Subjects/methods: In this multicenter cross-sectional study, we recruited 241 patients, 96 females, and 145 males; the mean age at onset was 59.9 ± 11.8 years. According to El Escorial criteria, 74 were definite ALS, 77 probable, 55 possible, and 35 suspected; 187 patients had spinal onset and 54 bulbar. Patients were categorized into three groups, according to their ΔFS : slow (81), intermediate (80), and fast progressors (80).Results: Current coffee consumers were 179 (74.3%), 34 (14.1%) were non-consumers, and 22 (9.1%) were former consumers, whereas six (2.5%) consumed decaffeinated coffee only. The log-ΔFS was weakly correlated with the duration of coffee consumption (p = 0.034), but not with the number of cup-years, or the intensity of coffee consumption (cups/day). Current tea consumers were 101 (41.9%), 6 (2.5%) were former consumers, and 134 (55.6%) were non-consumers. Among current and former consumers, 27 (25.2%) consumed only green tea, 51 (47.7%) only black tea, and 29 (27.1%) both. The log-ΔFS was weakly correlated only with the consumption duration of black tea (p = 0.028) but not with the number of cup-years.Conclusions: Our study does not support the hypothesis that coffee or tea consumption is associated with the ALS progression rate. Amyotrophic lateral sclerosis (ALS) is an untreatable neurodegenerative disease characterized by progressive degeneration of motor neurons in the spinal cord and motor cortex. The main clinical predictors of progression are age and site of onset, diagnostic delay, and the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) baseline score . The rolStill, there are no studies regarding the influence of coffee and tea consumption on ALS progression. The risk factors for progression may not be the same as for disease susceptibility; we aimed to assess a possible role of lifetime coffee and tea consumption on ALS progression.The study was designed as a cross-sectional multicenter study. It was conducted in three Centers in Italy—San Giovanni Rotondo (Coordinating Center), Novara, and Modena—one in the Republic of Moldova (Chisinau), and one in Romania (Cluj-Napoca). The study was approved by the Institutional Review Boards of the coordinating Center (N96/CE/2016) and the other four Centers. Written informed consent was obtained from all participants.Patients were recruited from October 2016 to January 2020, in different periods in each Center. Inclusion criteria were age higher than 18 years, diagnosis according to the El Escorial criteria , and conFor each patient, we collected demographics  and clinical data . Disease severity was estimated through ALSFRS-R, which evaluates the severity of the disease through a 12-item questionnaire . The ratCigarette smoking and alcohol consumption histories were evaluated with the “Questionnaire of Lifestyle,” which is part of the European Prospective Investigation into Cancer and Nutrition project study , 14. We Coffee and tea consumption histories were evaluated with a questionnaire built in analogy to the “Questionnaire of Lifestyle” asking patients whether they consumed or had consumed in the past coffee (regular or decaffeinated) or tea (green or black tea) and, if so, how many cups per day. One standard unit is equivalent to one cup of coffee or tea  . DetaileFor each current or former consumer, a cumulative lifetime load was computed for each beverage as the weighted sum of the number of cups consumed per year within each decade (six age periods), with weights equal to the number of years spent consuming in the decade (cup-years). This is the measure of the amount that a person has consumed over a lifetime and was computed by dividing the cumulative lifetime load by 365.25. We also calculated two measures of average daily consumption (cups/day): the lifetime intensity  during tThe questionnaire was designed in Italian, then translated in Romanian by a mother-language translator, and back-translated by an Italian mother-language translator. Two raters, previously trained in the use of the questionnaire and blinded to the patients' clinical status interviewed patients in a dedicated room. To evaluate the reliability of the questionnaire, two pairs of raters interviewed healthy subjects or patients with neurological diseases before the study started (40 in Chisinau and 25 in San Giovanni Rotondo). The sequence of interviews was randomized, and the randomization list was concealed. Each rater did the interviews on at least 1 day and no more than 7 days apart; this was considered a sufficient time window for the subjects being unable to remember their answers and not to change their consumption habits. Agreement between two raters for consumption (yes/no) was calculated with Cohen's kappa statistics  and was The patients' characteristics are reported as mean ± standard deviation, or median with interquartile range (IQR), depending on their distribution, for continuous variables, and with absolute and relative frequencies (%) for categorical variables. The normality of continuous variables distribution was checked by the Q–Q plot and the Shapiro–Wilk test. In the presence of right-skewed continuous variables, statistical analyses were performed on log values. Comparisons between two categorical variables were assessed by chi-square or Fisher exact tests, whereas comparisons between a continuous and a categorical variable were assessed by univariable and multivariable ANOVA models. Pairwise comparisons between groups of the categorical variables were performed, and, if necessary, least-square means of the dependent variable  were estimated for each level of the categorical variable. The standardized mean difference was further reported to describe clinical characteristics and was computed as the average of all possible standardized mean differences across pairwise comparisons. Correlation between two continuous variables was assessed by the Pearson correlation coefficient.p-value < 0.05 was considered for statistical significance. All statistical analyses were performed using SAS Release 9.4 . Conditional random forests and plots were performed using R Foundation for Statistical Computing .To visually assess the relationship between drink dose  and ΔFS or duration of drink consumption, boxplots and scatterplots with fitted regression line were depicted into a plot matrix. To detect all clinical, demographical, pathological, treatment, and lifestyle variables, which were mostly associated with (log-transformed) ΔFS, the conditional random forest (RF) algorithm  with 100We recruited 241 patients, 145 men and 96 women, with a sex ratio of 1.5:1. Onset was in the spinal district in 187 (77.6%) and bulbar in 54 (22.4%). The mean age was 59.9 ± 11.8 years at onset and 62.4 ± 11.1 at recruitment. The median time elapsed between disease onset to recruitment was 20 months (range 1.7–273). According to El Escorial criteria, 74 (30.7%) patients were categorized as definite ALS, 77 (32.0%) as probable, 55 (22.8%) as possible, and 35 (14.5%) as suspected. Other demographic and clinical characteristics are shown in r = 0.15, p = 0.034), but not with the number of cup-years, or the intensity of coffee consumption.Current coffee consumers were 179 (74.3%), 34 (14.1%) were non-consumers, and 22 (9.1%) former consumers, whereas six patients (2.5%) consumed decaffeinated coffee only. No patients started consuming coffee after ALS diagnosis. r = 0.25, p = 0.028).Current tea consumers were 101 (41.9%), 6 (2.5%) patients were former consumers, and 134 (55.6%) were non-consumers. Among 107 current and former consumers, 27 (25.2%) consumed only green tea, 51 (47.7%) only black tea, and 29 (27.1%) consumed both. No patients started consuming tea after ALS diagnosis. The VIMP provided by the conditional RF algorithm that we used to detect the variables most associated with (log-transformed) ΔFS suggested that age at onset, education, and site of onset were covariates, which explained the largest amount of the log-ΔFS variance . SpecifiWe found no correlation between coffee or tea consumption and disease progression. Log-ΔFS was only weakly correlated with the duration of coffee and black tea consumption, but not with the number of cup-years or cups/day. Coffee and tea consumptions have been studied in ALS for their possible role in the risk of developing the disease, but their possible role as predictors of the disease course once it has begun has not been evaluated so far. A pooled analysis based on over 1,000,000 individuals from five cohorts  did not To analyze the possible role of beverages on disease progression, we divided log-ΔFS into tertiles. Tertiles of ΔFS distribution are associated with survival , 24, indTo analyze a possible interaction of coffee and tea consumptions with other lifestyle factors and clinical variables, we firstly ranked variables, using a variable importance measure, and eventually performed a multivariable model. None of the lifestyle variables analyzed was ranked high. Clinical/demographic variables, such as age at onset, site of onset, and education, explained the largest amount of log-ΔFS variance. Adjusting for potential clinical and demographic confounders, the multivariable analysis did not show any association between coffee and tea consumption and log ΔFS.The limitations of our study are related to a possible recall bias, which seems improbable given that patients were unaware of the study hypothesis, and interviewers were blinded to clinical history and neurological status. We could not evaluate the influence of unmeasured variables, such as physical activity, trauma, and diet, but it is unlikely that these are confounders of coffee or tea consumption.Although the findings should be interpreted with caution, this study has several strengths. Selection bias was minimized because patients were consecutively enrolled at five different Centers and included a large spectrum of disease severity. Previous cohort and case–control studies only assessed the baseline intake of coffee and tea, but not the personal history of consumption for every single patient. On the contrary, we studied both the exposures at the time of the interview, and their lifetime cumulative effect, using a cup-year measure in analogy to pack-year research on smoking.Our study does not support the hypothesis that coffee or tea intake is associated with a different ALS progression, contrarily to other neurodegenerative diseases. Although our findings seem rather strong, we cannot exclude a possible effect of coffee or tea on a subgroup of patients, for example, with positive family history. However, this could only be studied with a much larger sample of patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Boards of the coordinating Center (N96/CE/2016). The patients/participants provided their written informed consent to participate in this study.AC: study design, recruitment, examination of patients, data collecting, management, analysis, final approval of the version to be published, drafting the paper, and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. AI: study design, management, analysis, final approval of the version to be published, drafting the paper, and writing the paper. AF: study design, data analysis, data interpretation, and drafting the paper. SR: data set, data analysis, and data interpretation. LM: conception of the study, recruitment, examination of patients, data collecting, and writing. JM: recruitment, examination of patients, data collecting, and final approval of the version to be published. VL and DM: contribution to the conception of the study, final approval of the version of the manuscript to be published, and revising critically the research and the manuscript. ML: study design, recruitment, data management, analysis, drafting the paper, and final approval of the version to be published. 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.


Coronavirus disease 2019 (COVID-19) patients suffer from anxiety, depression, and sleep disorder due to isolation treatment and other reasons. Whether life interventions can be an alternative therapy for COVID-19 patients, accompanied with anxiety, depression, and sleep disorder, is controversial. Therefore, we conducted a meta-analysis and systematic review to evaluate the effects of life interventions on anxiety, depression, and sleep disorder in COVID-19 patients to provide some guidance for clinical application.The randomized controlled trials related to the life intervention and COVID-19 from inception to February 2021 will be searched. The following databases are our focused areas: the Cochrane Central Register of Controlled Trials, PubMed, MEDLINE, EMBASE, Web of Science, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and Wan Fang Database. Two investigators would independently screen the literature according to the inclusion and exclusion criteria, extract data, and evaluate the risk of bias in the included studies. Meta-analysis was performed with RevMan 5.3 software.The results will provide a high-quality synthesis of current evidence for researchers in this subject area.The conclusion of our study will provide evidence for the judgment of whether life intervention is an effective intervention on COVID-19 patients.CRD42020199802. COVID-19 and the closed environment of isolation wards will adversely affect patients psychologically. After hospitalization, patients left the familiar working and living environment, and was isolated and closed, so the original way of life was completely disrupted. In addition, the monotonous life in the isolation ward and the lack of recreational activities caused the psychological disorder of patients. Psychological disorder can lead to low immunity and reduce enthusiasm for treatment, which posed negative impacts on the rehabilitation of the disease. A retrospective study revealed that isolation treatment may cause unexpected mental trauma to patients, and may even result in self-harm behaviors such as suicide, and these effects can still exist 3 years after the release of quarantine. Through clinical observation, many patients developed anxiety, depression, and sleep disorder after isolation treatment. As psychological stresses, anxiety, and depression will cause a series of physiological events and the decline of immunity.,18 Treating and nursing patients in isolation ward are mainly aimed at the disease itself, and do not pay enough attention to the physical and mental impacts caused by isolation.,20 Improving treatment environment in the isolation area and simulating the normal work and rest rules as far as possible are of great significance in relieving emotional tension, dredging psychological disorder, and promoting disease rehabilitation. With the improvement of COVID-19 condition and the experience of discharged patients, timely life intervention can improve the prognosis, maximize the preservation of function, and improve the life quality and psychological function.At present, the main source of infection is COVID-19 infection, dominantly through respiratory droplets and close contact.At present, there is a lack of evidence-based medicine data on the effects of life intervention on anxiety, depression, and life quality in COVID-19 patients. Therefore, in this study, we aimed to systematically review the effects of life intervention on anxiety, depression, and life quality in COVID-19 patients.22.1The systematic review protocol has been registered in PROSPERO, and the registration number is CRD42020199802. The consent of this protocol report is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols Statement Guidelines.2.2Articles related to the influence of life intervention on COVID-19 patients will be included. Due to language restrictions, we would search for articles in English and Chinese, so as to get a more objective and true evaluation. All articles included are randomized controlled trial (RCT) type articles.2.2.1Inclusion criteria:(1)Patients with confirmed diagnosis of COVID-19;(2)Patients >65 years old;(3)Patients >6 months after the onset of other acute diseases;(4)Patients with a mini-mental state examination score >21;(5)Patient without COPD or any other respiratory diseases;(6)Patients with a forced expiratory volume in 1 second >70%.Exclusion criteria:(1)Patients with moderate or severe heart disease ;(2)Patients with severe ischemic or hemorrhagic stroke or neurodegenerative diseases.2.2.2The interventions will be life interventions either alone or combined with other therapies. The categories of intervention are the modification of patients and caregiver behaviors and the home-based environment, and hospital-based environmental control measures. The control group included patients who did not receive the above-mentioned life interventions. If possible, we will assess whether the intervention group and the control group follow the same protocol in other aspects of patients’ care. We will describe the interventions adopted by each institute and included in the study.2.2.3Anxiety, depression, life quality, and sleep quality scale.2.3The following electronic databases will be searched from inception to February 2021: the Cochrane Central Register of Controlled Trials, PubMed, MEDLINE, EMBASE, Web of Science, China National Knowledge Infrastructure, Chinese Biomedical Literature Database, and Wan Fang Database. About other sources, we also plan to manually search for the unpublished conference articles and the bibliography of established publications.2.4The search terms on PubMed are as follows: “lifestyle intervention” or “life intervention” and “lifestyle modification” or “life modification”; COVID-19 ; RCT . The combination of Medical Subject Headings (MeSH) and text words will be used. These search terms are summarized in Table 2.52.5.1All reviewers received evidence-based training and adhered to the process summarized. The 2 reviewers independently screened the literature based on the title, abstract, and keywords of literatures, and excluded the irrelevant literatures. The rest of literatures were further confirmed by 2 researchers after reading the full text. The excluded research and the reasons for the exclusion were recorded. The differences between the 2 reviewers were resolved through consensus or by a third independent reviewer. The process of the selection is displayed in Figure 2.5.2Two researchers independently screened the literature and extracted data. If there were differences, a third researcher was asked to judge. Extracted contents include author, publication date, country, sample size, age, type, content and duration of life intervention, type of anxiety, depression, life quality and sleep quality scale, result measurement data, intervention time and details, etc.2.5.3The risk of bias assessment method set out in the Cochrane Handbook for Systematic Reviews of Interventions 5.1.0 will be adopted on the included RCTs. The main items to be considered are as follows:1)randomization plan;2)group concealment;3)blinding method;4)incomplete data reporting;5)selective outcome report; and6)other sources of bias.Each item will be evaluated as “high,” “low,” or “unclear.”2.5.4Standardized mean difference is applied to measure the efficacy of 95% confidence interval.2.5.5We will include data from parallel group design studies for meta-analysis. In these trials, we will collect and analyze individual measurements of each outcome for each participant.2.5.6We will try our best to ensure the integrity of the data. If the included RCT data are not complete, we will try every means to contact the corresponding author of the article by sending emails or making a phone call. If the corresponding author cannot be contacted, we will remove the experiment with incomplete data. After data integrity is assured, intention analysis therapy and sensitivity analysis will be performed.2.5.7χ2 test (the test level was α = 0.1) and combining with I2 to quantitatively determine the size of the heterogeneity. When P < .1 and/or I2 > 50%, the random effect model is used for the combined analysis. Otherwise, the fixed-effect model is applied for the combined analysis.The heterogeneity included in the results of the study was analyzed by carrying out the 2.5.8Publication bias was evaluated by conducting the funnel plot asymmetry test.2.5.9We will use Review Manager Software (RevMan) V.5.3  and Stata 14.0 software  for data analysis and quantitative data synthesis. If there are no findings of statistical heterogeneity, the fixed-effect model will be adopted for data synthesis. If there is significant statistical heterogeneity, we will use the random effect model, and all participants will explore the possible causes from a clinical and methodological perspective and provide a descriptive or subgroup analysis.2.5.10A subgroup analysis will be carried out on the basis of intervention time (<6 weeks or ≥6 weeks).2.5.11To determine the stability of the outcome measures, each outcome measure was analyzed using sensitivity analysis.2.5.12 The results of the meta-analysis in RevMan software were imported into Grading of Recommendations Assessment, Development, and Evaluation pro software. The quality of evidence will be divided into high quality, medium quality, low quality, and very low quality.According to the Grading of Recommendations Assessment, Development, and Evaluation system, the quality of evidence and the level of recommendation are evaluated.2.5.13The content of this article does not involve moral approval or ethical review and will be presented in print or at relevant conferences.3–27 It is a great disaster to individuals, families, and society as well. In order to cut off the route of transmission, it is required to isolate and treat suspected and confirmed cases. In the process of isolation, fear and inferiority caused by infection, worry about the safety of family members, boredom caused by a long isolation period, economic losses caused by the epidemic, closed treatment environment, and so on will have serious impacts on patients psychologically.,30 Therefore, there are negative emotions such as anxiety, depression, fear, and so on. Negative psychology seriously affects the recovery of the disease. A good life style and diversified hospital activities are conductive to patients to adapt to the hospital environment, and are of great significance to improve their life quality and promote physical and mental rehabilitation. At present, there is no meta-analysis to the effects of life intervention on anxiety, depression, and life quality in COVID-19 patients. It is hoped that this meta-analysis can provide a convincing scientific basis and guide clinical practices.COVID-19 is mainly transmitted through the respiratory tract, with the characteristics of strong infectivity and great harmfulness.This study has some limitations. This study only searches Chinese and English databases, which may be incomplete. In this meta-analysis, there are few studies that can carry out meta-analysis, and the information provided by the literature is also limited. We are unable to analyze the indicators we are concerned about through subgroup analysis. Different life intervention methods, differences in the intervention process, and defects in the experimental design may lead to certain heterogeneity of the results.To sum up, life intervention for COVID-19 patients may help patients adjust their mentality, reduce their anxiety and depression, and improve their life quality.Conceptualization: Yujiao Fang.Data curation: Yuli Qian, Huan Xu, Qiaozhen Li.Formal analysis: Qiaozhen Li.Funding acquisition: Yujiao Fang.Investigation: Qian Zhan.Methodology: Yuli Qian, Jing Diao.Project administration: Yujiao Fang.Resources: Yuli Qian, Qian Zhan.Supervision: Huan Xu, Jing Diao.Validation: Huan Xu, Jing Diao.Writing – original draft: Yujiao Fang, Yuli Qian.Writing – review & editing: Yujiao Fang, Yuli Qian.


The EAT-Lancet Commission has proposed a model diet to improve the health of human beings and that of the planet. Recently, we proposed the Planetary Health Diet Index (PHDI) to assess adherence of the population to this model diet. In this study, we aimed to evaluate adherence to the PHDI and obesity outcomes using baseline data from 14,515 participants in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). The dietary data were assessed using a 114-item FFQ. Body mass index (BMI) and waist circumference (WC) were both used continuously and categorized. Linear and multinomial regression models adjusted for potential confounding factors were performed to assess the relationship between adherence to PHDI and outcomes. An inverse association was observed between adherence to PHDI and obesity indicators. Individuals with high adherence to the PHDI had lower BMI (β−0.50 95% CI−0.73:−0.27) and WC (β−1.70 95% CI−2.28:−1.12) values. They were also 24% less likely to be overweight (OR 0.76 95% CI 0.67:0.85) or obese (OR 0.76 95% CI 0.65:0.88), and they were 14% and 27% less likely to have increased WC (OR 0.86 95% CI 0.75:0.98) or substantially increased WC (OR 0.73 95% CI 0.64:0.83) than those with lower adherence. Our results showed that higher adherence to the PHDI may decrease obesity indicators. Early in 2019, the EAT-Lancet Commission on “Healthy Diets from Sustainable Food Systems” (EAT-Lancet) published a scientific report on sustainable diets and proposed a healthy reference diet for human and planetary health, called the "planetary health diet" (PHD) [The EAT-Lancet report estimates that following the recommendations for a healthy and sustainable diet could prevent 11 million deaths per year  and it hThe main purpose of the EAT-Lancet diet is to improve the health of the population and the planet, and for that, the report confirms that this reference diet is nutritionally balanced and has a low environmental impact. However, one study noted that adopting the PHD on a global scale would have different impacts on greenhouse gas emissions (GHGE) . StudiesConsequently, we recently proposed the Planetary Health Diet Index (PHDI), which consists of 16 components that score proportionally and consider all EAT-Lancet food groups in addition caloric density . PHDI scThis study is a cross-sectional analysis of the baseline data from ELSA-Brasil, a multicenter and ongoing cohort study conducted in six Brazilian cities  from three major Brazilian regions . The study design and data collection were described previously ,17,18,19Food consumption was assessed using a previously developed and validated semi-quantitative FFQ with 114 food items ,21. ThisThe daily consumption of each FFQ item (in g/day) was obtained by multiplying the portion size by the corresponding frequency. The food measurements were then converted into nutrient intake using the United States Department of Agriculture (USDA) Food Composition Database, except when its values were outside the range of 80% to 120% of those described in the Brazilian Table of Food Composition, where the latter reference was used. The Planetary Health Diet Index (PHDI) is based on the recommendations of the reference diet proposed by the EAT-Lancet Commission . Its devEach one of the 16 components can score proportionally between 0 and 5 or 10 points. The components in the adequacy, optimum and moderation categories can reach a maximum of 10 points, while the ratio components can score a maximum of 5 points, resulting in a total score from 0 to 150 points. More information regarding the cutoff points and scoring criteria can be obtained from Cacau et al. . Figure ®, model 2096PP) was used, with a capacity of 200 kg and a precision of 50 g. The height was measured with a wall stadiometer  with a precision of 1 mm, attached to the wall, with the individual in a supine position, barefoot, leaning their head, buttocks, and heels against the wall and staring in the horizontal plane. The WC was measured with the participant fasted and with an empty bladder, standing upright, breathing normally, with their feet together, part of their standard uniform lifted, and with their arms crossed in front of their chest. The measurement was taken at the midpoint between the crest and the lower edge of the costal arch, using an inextensible tape. The anthropometric measures of weight, height, and waist circumference (WC) were obtained using international criteria and standards techniques . The bod2) [2 to <30 kg/m2 was considered overweight, and ≥30 kg/m2 as obesity [The Body Mass Index (BMI) was calculated as weight (kg) divided by squared height (m2) . The BMI obesity . A WC <8 obesity .Each participant was interviewed at his or her workplace and visited the research center for clinical examinations, according to standard protocols. These interviews focused on sociodemographic characteristics, which were obtained using a general questionnaire . The socSmoking was stratified by nonsmokers, ex-smokers, and current smokers. Alcohol consumption was dichotomized into yes or no based on excessive alcohol consumption  ,23. The Hypertension was defined as the reported use of medications to treat hypertension, a systolic blood pressure ≥140 mmHg, or a diastolic blood pressure ≥90 mmHg . DiabeteWe compared the baseline characteristics of the participants according to quintiles of the PHDI scores. We calculated means and standard deviations (SD) or percentages for each variable across the quintiles. ANOVA or Pearson's chi-square tests were used to assess the statistical significance of differences between means or proportions, respectively.Multiple linear regression models were used to assess the associations between PHDI scores and BMI and WC, respectively. Multiple multinomial logistic regression models were used to determine associations between the PHDI scores and the categories of BMI and WC. We used the PHDI scores, both categorized into quintiles and continuous , as explanatory variables. All the models were presented as age-adjusted models and fully adjusted models with sex, self-reported race, per capita family income, smoking, sporadic alcohol intake, diabetes, hypertension, dyslipidemia, total energy intake and dietary changes in the last six months, in addition to the age adjustment. We performed a sensitivity analysis excluding individuals below p1 and above p99 for total energy intake, as under- or over-reporters, respectively.® , version 14.2 and the p-value <0.05 was considered statistically significant.All the statistical analyses were performed using STATAThe baseline characteristics of the participants, according to the quintiles of the PHDI scores, are summarized in 2 for BMI (95%CI−0.73:−0.27) and −1.70 cm (95% CI−2.28:−1.12) for WC, when compared to individuals in the first quintile. An overall significant linear trend was observed for the association between the quintiles of the PHDI scores and the two outcomes (p < 0.001), even with the non-significance of some terciles. In addition, there was a decrease of −0.15 kg/m2 (95% CI−0.21:−0.08) in the BMI and −0.52 cm (95% CI−0.68:−0.36) in the WC for each 10-point increase in the PHDI score in the fully adjusted models.p < 0.001), obesity (p < 0.001), and substantially increased abdominal obesity (p < 0.001). The multinomial regression models suggested that higher PHDI scores  were associated with a lower likelihood of obesity when compared with those in the first quintile . The subIn the sensitivity analysis, excluding individuals below p1 and above p99 from total energy intake had little effect on the magnitude of the associations, keeping the same directions for the associations between obesity and PHDI .This is the first study to assess the association of a dietary caloric density index that scores gradually to assess adherence to EAT-Lancet recommendations with obesity outcomes. As a result, we observed an inverse association between adherence to EAT-Lancet recommendations–evaluated by higher scores on the planetary health diet index (PHDI)–and obesity indicators assessed by BMI and WC, both continuously and categorized, in participants from a large multicentric ongoing cohort study in Brazil. In our previous study, with the same population, we observed that higher PHDI scores were associated with higher overall dietary quality and lower GHGE emissions . These rFew studies evaluated the association between healthy and sustainable diets and obesity and, as far as we know, only two studies evaluated the relationship between the EAT-Lancet recommendations and obesity indicators ,25. Sham2 in the BMI of individuals with ≥12 points on the EAT-Lancet diet score, a binary index that features14 components expressed in grams. These results are similar to those found in our study. However, the two indices mentioned use a binary scoring system and do not allow a better distribution of the population's adherence to dietary recommendations [In another study, which evaluated 46,069 participants enrolled throughout the UK, in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Oxford study, Knuppel et al.  observedndations . The PHDIn addition to the aforementioned studies, which evaluated the relationship between obesity indicators and EAT-Lancet-based indices, there was a population-based study, carried out by Seconda et al. , which aDespite the limited number of studies evaluating the relationship between the EAT-Lancet recommendations or adherence to sustainable diets and obesity, there are studies that relate diets considered sustainable, such as the Mediterranean diet ,29,30 anHowever, despite the promising results, we observed that most of the population evaluated only achieved half of the possible points on the PHDI , indicating that this population had a low adherence to the recommendations for a healthy and sustainable diet . A possiOur study featured some strengths, such as its use of a validated index that associated higher overall dietary quality and lower GHGE emissions. In addition, we used data from a well-established multicenter cohort study in Brazil, which followed strict data collection and processing protocols. However, some limitations may be noted as well. For instance, these results must be considered within the context of the study design. This was a cross-sectional analysis, which can evaluate association, but not causality. In addition, food consumption was assessed using an FFQ, an instrument that, despite being one of the most commonly used methods in epidemiological studies for evaluating the relationship between diet and health outcomes, still features some limitations, such as the finitude of its foods list and dietary misreporting bias. ELSA-Brasil is designed to assess risk factors and associations with cardiovascular diseases and diabetes, but it is not a population-based study, despite being a multicenter study from six different Brazilian cities across three major Brazilian regions. However, this characteristic allowed the inclusion of a population whose ethnic and social diversity was similar to that of heterogeneous populations, mainly of middle income, who live in large cities in Brazil. Thus, our external validity can be extended to urban centers with similar characteristics inside and outside Brazil.In conclusion, we observed that higher PHDI scores were inversely associated with excess weight and obesity, as measured by both BMI and WC. These results demonstrate that the recommendations for a healthy and sustainable diet proposed by the EAT-Lancet have beneficial effects on human health, as they decrease the likelihood of being overweight or obese, which are risk factors for non-communicable chronic diseases, such as diabetes and cardiovascular disease. Furthermore, these results can offer important support for public policy planning and guidelines on the benefits of healthy and sustainable diets, with an emphasis on the proposal by the EAT-Lancet Commission.