Identification of novel variants associated with osteoporosis, type 2 diabetes and potentially pleiotropic loci using pleiotropic cFDR method

Understanding the relationship between inflammation, apoptosis, and diabetes osteoporosis: A bioinformatics approach and experimental verification

Diabetes osteoporosis (DOP) is a chronic metabolic bone disease. This study aimed to identify potential biomarkers of DOP and explore their underlying mechanisms through bioinformatics methods and experimental verification. Bioinformatics methods were used to identify differentially expressed genes (DEGs) for DOP based on GEO data and the GeneCards database. GO and KEGG enrichment analyses were used to search the key pathways. The STRING website was used to construct a protein-protein interaction (PPI) network and identify key genes. Then, 50 mg/mL glucose was used to interveneosteoblasts (OBs).CCK-8 and Alizarin Red staining were used to investigate the proliferation and differentiation changes in OBs. Flowcytometry was used to investigate apoptosis. The membrane protein chip, WB, and RT-PCR were used to verify the expression of key targets or pathways about DOP. Forty-two common genes were screened between DOP-related targets and DEGs. GO and KEGG enrichment analysis showed that DOP was mainly associated with cytokine-cytokine receptor interactions, and apoptosis. PPI network analysis showed that TNF, IL1A, IL6, IL1B, IL2RA, Fas ligand (FASLG), and Fas cell surface death receptor (FAS) were key up-regulated genes in the occurrence of DOP. The experiment results show that 50 mg/mL glucose significantly inhibited OBs proliferation but presented an increase in apoptosis. Membrane protein chip, WB, and RT-PCR-verified a significantly active in the expression of TNF/FASLG/FAS pathway. High glucose activated the TNF-α/FAS/FASLG pathway and induced the inflammatory microenvironment and apoptosis, then impaired osteogenic differentiation of OBs. These may be an important mechanism for the occurrence and development of DOP.

Exploration of shared gene signatures and molecular mechanisms between type 2 diabetes and osteoporosis

Type 2 diabetes mellitus (T2D) and osteoporosis (OP) are systemic metabolic diseases and often coexist. The mechanism underlying this interrelationship remains unclear. We downloaded microarray data for T2D and OP from the Gene Expression Omnibus (GEO) database. Using weighted gene co-expression network analysis (WGCNA), we identified co-expression modules linked to both T2D and OP. To further investigate the functional implications of these associated genes, we evaluated enrichment using ClueGO software. Additionally, we performed a biological process analysis of the genes unique in T2D and OP. We constructed a comprehensive miRNA-mRNA network by incorporating target genes and overlapping genes from the shared pool. Through the implementation of WGCNA, we successfully identified four modules that propose a plausible model that elucidates the disease pathway based on the associated and distinct gene profiles of T2D and OP. The miRNA-mRNA network analysis revealed co-expression of PDIA6 and SLC16A1; their expression was upregulated in patients with T2D and islet β-cell lines. Remarkably, PDIA6 and SLC16A1 were observed to inhibit the proliferation of pancreatic β cells and promote apoptosis in vitro, while downregulation of PDIA6 and SLC16A1 expression led to enhanced insulin secretion. This is the first study to reveal the significant roles of PDIA6 and SLC16A1 in the pathogenesis of T2D and OP, thereby identifying additional genes that hold potential as indicators or targets for therapy.

Type 2 diabetes mellitus and the risk of male infertility: a Mendelian randomization study

Objective

To assess the causal effect of type 2 diabetes mellitus (T2DM) on male infertility (MI) and erectile dysfunction (ED) by Mendelian randomization (MR) analysis.

Methods

Data for T2DM, MI, and ED were obtained from genome-wide association studies (GWAS) involving 298, 957, 73, 479, and 223, 805 Europeans, respectively. We performed univariate MR analysis using MR Egger, Weighted median (WM) and Inverse variance weighted (IVW) methods to assess causal effects among the three. Through the Genotype Tissue Expression (GTEx) database, single-nucleotide polymorphisms (SNPs) that affect the expression levels of T2DM-related genes were located using expression quantitative trait loci (eQTL).

Results

MR analysis showed a significant causal relationship between T2DM and ED (WM, OR: 1.180, 95%CI: 1.010-1.378, P = 0.037; IVW, OR: 1.190, 95%CI: 1.084-1.300, P < 0.001). There is also a significant causal relationship between T2DM and MI (MR Egger, OR: 0.549, 95%CI: 0.317-0.952, P = 0.037; WM, OR: 0.593, 95%CI: 0.400, P = 0.010; IVW, OR: 0.767, 95%CI: 0.600-0.980, P = 0.034). ED may not cause MI (P > 0.05). We also found that rs6585827 corresponding to the PLEKHA1 gene associated with T2DM is an eQTL variant affecting the expression of this gene.

Conclusion

T2DM has a direct causal effect on ED and MI. The level of PLEKHA1 expression suppressed by rs6585827 is potentially associated with a lower risk of T2DM.

Epigenome-wide analysis identifies methylome profiles linked to obsessive-compulsive disorder, disease severity, and treatment response

Obsessive-compulsive disorder (OCD) is a prevalent mental disorder affecting ~2-3% of the population. This disorder involves genetic and, possibly, epigenetic risk factors. The dynamic nature of epigenetics also presents a promising avenue for identifying biomarkers associated with symptom severity, clinical progression, and treatment response in OCD. We, therefore, conducted a comprehensive case-control investigation using Illumina MethylationEPIC BeadChip, encompassing 185 OCD patients and 199 controls recruited from two distinct sites in Germany. Rigorous clinical assessments were performed by trained raters employing the Structured Clinical Interview for DSM-IV (SCID-I). We performed a robust two-step epigenome-wide association study that led to the identification of 305 differentially methylated CpG positions. Next, we validated these findings by pinpointing the optimal set of CpGs that could effectively classify individuals into their respective groups. This approach identified a subset comprising 12 CpGs that overlapped with the 305 CpGs identified in our EWAS. These 12 CpGs are close to or in genes associated with the sweet-compulsive brain hypothesis which proposes that aberrant dopaminergic transmission in the striatum may impair insulin signaling sensitivity among OCD patients. We replicated three of the 12 CpGs signals from a recent independent study conducted on the Han Chinese population, underscoring also the cross-cultural relevance of our findings. In conclusion, our study further supports the involvement of epigenetic mechanisms in the pathogenesis of OCD. By elucidating the underlying molecular alterations associated with OCD, our study contributes to advancing our understanding of this complex disorder and may ultimately improve clinical outcomes for affected individuals.

Genome-Wide Association Study of Growth Traits in a Four-Way Crossbred Pig Population

Growth traits are crucial economic traits in the commercial pig industry and have a substantial impact on pig production. However, the genetic mechanism of growth traits is not very clear. In this study, we performed a genome-wide association study (GWAS) based on the specific-locus amplified fragment sequencing (SLAF-seq) to analyze ten growth traits on 223 four-way intercross pigs. A total of 227,921 highly consistent single nucleotide polymorphisms (SNPs) uniformly dispersed throughout the entire genome were used to conduct GWAS. A total of 53 SNPs were identified for ten growth traits using the mixed linear model (MLM), of which 18 SNPs were located in previously reported quantitative trait loci (QTL) regions. Two novel QTLs on SSC4 and SSC7 were related to average daily gain from 30 to 60 kg (ADG30-60) and body length (BL), respectively. Furthermore, 13 candidate genes (ATP5O, GHRHR, TRIM55, EIF2AK1, PLEKHA1, BRAP, COL11A2, HMGA1, NHLRC1, SGSM1, NFATC2, MAML1, and PSD3) were found to be associated with growth traits in pigs. The GWAS findings will enhance our comprehension of the genetic architecture of growth traits. We suggested that these detected SNPs and corresponding candidate genes might provide a biological foundation for improving the growth and production performance of pigs in swine breeding.

Bone mineral density and risk of breast cancer: A cohort study and Mendelian randomization analysis

BACKGROUND

Estrogen is involved in both bone metabolism and breast cancer proliferation. However, evidence about the risk of breast cancer according to women's bone mineral density (BMD) is scarce, and little is known about their causal associations.

METHODS

Women participating in the UK Biobank cohort were used to investigate the association between BMD and the risk of breast cancer using Cox regression models. Instrumental variants associated with estimated BMD (eBMD) were extracted from genome-wide association studies with European ancestry. Logistic regression was used to calculate the genetic association with breast cancer in the UK Biobank and 2-sample Mendelian randomization (MR) analyses to assess their causal associations with breast cancer. Finally, the pleiotropic conditional false discovery rate (cFDR) method was conducted to further detect common genetic variants between BMD and breast cancer.

RESULTS

Compared with the general population, postmenopausal women with BMD T scores <-2.5 had a lower risk of breast cancer (hazard ratio [HR], 0.77; 95% CI, 0.59-1.00), and this effect was stronger in women with fracture (HR, 0.31; 95% CI, 0.12-0.82). In MR analysis, no causal associations between eBMD and breast cancer were observed. The cFDR method identified 63 pleiotropic loci associated with both BMD and breast cancer, of which CCDC170, ESR1, and FTO might play crucial roles in their pleiotropy.

CONCLUSIONS

An association between BMD and the risk of postmenopausal breast cancer in the UK Biobank was observed, whereas no evidence supported their causal association. Instead, their association could be explained by pleiotropic genetic variants leading to the pathology of osteoporosis and breast cancer.

Multiomics Integrated Analysis Identifies SLC24A2 as a Potential Link between Type 2 Diabetes and Cancer

Background

So far, type 2 diabetes (T2D) is considered as an independent risk factor for various cancers, but the underlying mechanism remains unclear. Methods. SLC24A2 was first identified as a key gene strongly associated with fasting plasma glucose (FPG). Then, overlapped differentially expressed genes (DEGs) between T2D verse control and SLC24A2-high verse SLC24A2-low were extracted and imported into weighted correlation network analysis. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for functional enrichment analysis of DEGs. Least absolute shrinkage and selection operator was utilized to build a T2D prediction model. Timer and K-M plotters were employed to find the expression and prognosis of SLC24A2 in pan cancer.

Results

Interestingly, both DEGs between T2D verse control and SLC24A2-high verse SLC24A2-low enriched in cancer-related pathways. Moreover, a total of 3719 overlapped DEGs were divided into 8 functional modules. Grey module negatively correlated with T2D and FPG and was markedly involved in ribosome biogenesis. Ten SLC24A2-related genes (RRP36, RPF1, GRWD1, FBL, EXOSC5, BCCIP, UTP14A, TWISTNB, TBL3, and SKIV2L) were identified as hub genes, based on which the LASSO model accurately predicts the occurrence of T2D (AUC = 0.841). In addition, SLC24A2 was only expressed in islet β cells and showed abnormal expression in 17 kinds of cancers and significantly correlated with the prognosis of 10 kinds of cancers.

Conclusion

Taken together, SLC24A2 may link T2D and cancer by influencing the ribosome function of islet β cells and play different prognostic roles in different cancers.

Identification of a Potential MiRNA-mRNA Regulatory Network for Osteoporosis by Using Bioinformatics Methods: A Retrospective Study Based on the Gene Expression Omnibus Database

INTRODUCTION

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass, impairment of bone microstructure, and a high global morbidity rate. There is increasing evidence that microRNAs (miRNAs) are associated with the pathogenesis of OP. Weighted gene co-expression network analysis (WGCNA) is a systematic method for identifying clinically relevant genes involved in disease pathogenesis. However, the study of the miRNA-messenger RNA (mRNA) regulatory network in combination with WGCNA in OP is still lacking.

METHODS

The GSE93883 and GSE7158 microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs) and differentially expressed genes (DEGs) were analyzed with the limma package. OP-related miRNAs from the most clinically relevant module were identified by the WGCNA method. The overlap of DE-miRNAs and OP-related miRNAs was identified as OP-related DE-miRNAs. Both upstream transcription factors and downstream targets of OP-related DE-miRNAs were predicted by FunRich. An intersection of predicted target genes and DEGs was confirmed as downstream target genes of OP-related DE-miRNAs. With the use of clusterProfiler in R, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on target genes. Finally, both the protein-protein interaction (PPI) network and miRNA-mRNA network were constructed and analyzed.

RESULTS

A total of 79 OP-related DE-miRNAs were obtained, most of which were predicted to be regulated by specificity protein 1 (SP1). Subsequently, 197 downstream target genes were screened out. The target genes were enriched in multiple pathways, including signaling pathways closely related to the onset of OP, such as Ras, PI3K-Akt, and ErbB signaling pathways. Through the construction of the OP-related miRNA-mRNA regulatory network, a hub network that may play a prominent role in the formation of OP was documented.

CONCLUSION

By using WGCNA, we constructed a potential OP-related miRNA-mRNA regulatory network, offering a novel perspective on miRNA regulatory mechanisms in OP.

A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

Identification of Novel Pleiotropic SNPs Associated with Osteoporosis and Rheumatoid Arthritis

Genome-wide association studies (GWASs) have identified hundreds of genetic loci for osteoporosis (OP) and rheumatoid arthritis (RA), individually, however, a large proportion of the total trait heritability remains unexplained. Previous studies demonstrated that these two diseases may share some common genetic determination and risk factors, but they were generally focused on individual trait and failed to identify the common variants that play key functional roles in the etiology of these two diseases. Here, we performed a conditional false discovery rate (cFDR) analysis to identify novel pleiotropic variants shared between them by integrating two independent GWASs with summary statistics for total body bone mineral density (TB-BMD, a major risk factor for osteoporosis) (n = 66,628) and RA (n = 58,284). A fine-mapping approach was also applied to identify the most probable causal variants with biological effects on both TB-BMD and RA. As a result, we found 47 independent pleiotropic SNPs shared between TB-BMD and RA, and 40 of them were validated in heel ultrasound estimated BMD (eBMD), femoral neck BMD (FN-BMD) or lumbar spine (LS-BMD). We detected one SNP (rs13299616) was novel and not identified by previous BMD or RA-related studies. Combined with fine-mapping and GWAS-eQTL colocalization analyses, our results suggested that locus 1p13.2 (including PTPN22, MAGI3, PHTF1, and RSBN1) was an important region to regulate TB-BMD and RA simultaneously. These findings provide new insights into the shared biological mechanisms and functional genetic determinants between OP and RA, and novel potential targets for treatment development.

Identification of novel functional CpG-SNPs associated with Type 2 diabetes and birth weight

Genome-wide association studies (GWASs) have identified hundreds of genetic loci for type 2 diabetes (T2D) and birth weight (BW); however, a large proportion of the total trait heritability remains unexplained. The previous studies were generally focused on individual traits and largely failed to identify the majority of the variants that play key functional roles in the etiology of the disease. Here, we aim to identify novel functional loci for T2D, BW and the pleiotropic variants shared between them by performing a targeted conditional false discovery rate (cFDR) analysis that integrates two independent GWASs with summary statistics for T2D (n = 26,676 cases and 132,532 controls) and BW (n = 153,781) which entails greater statistical power than individual trait analyses. In this analysis, we considered CpG-SNPs, which are SNPs that may influence DNA methylation status, and are therefore considered to be functionally important. We identified 103 novel CpG-SNPs for T2D, 182 novel CpG-SNPs for BW (cFDR < 0.05), and 52 novel pleiotropic loci for both (conjunction cFDR [ccFDR] < 0.05). Among the identified novel CpG-SNPs, 33 were annotated as methylation quantitative trait loci (meQTLs) in whole blood, and 145 displayed at least some effects on meQTL, metabolic QTL (metaQTL), and/or expression QTL (eQTL). These findings may provide further insights into the shared biological mechanisms and functional genetic determinants that overlap between T2D and BW, thereby providing novel potential targets for treatment/intervention development.

The efficacy and safety of traditional Chinese medicine's tonifying-kidney, strengthening-spleen, and invigorating-blood circulation (Bushen-Jianpi-Huoxue) principle for type 2 diabetes mellitus with osteoporosis: A protocol for systematic review and meta-analysis

BACKGROUND

Type 2 diabetes mellitus (T2D) and osteoporosis (OP) often coexist, and both are systemic metabolic diseases and seriously increase the risk of fragility fracture. However, there is no specific Western medicine for the treatment of T2D with OP (T2DOP). As reported in clinical and experimental studies, traditional Chinese medicine (TCM) based on principle of tonifying-kidney, strengthening-spleen, and invigorating blood circulation (Bushen-Jianpi-Huoxue) (BSJPHX) has significant efficacy against T2DOP. This protocol will be designed for a systematic review and meta-analysis to assess the efficacy and safety of TCM BSJPHX principle in the treatment of T2DOP.

METHODS

All relevant randomized controlled trials (RCTs) related to TCM therapies conducted in BSJPHX principle for T2DOP will be searched in the 8 electronic databases: PubMed, Cochrane Library, Wed of Science, EMBASE, Chinese National Knowledge Infrastructure Database (CNKI), Chinese Biomedical Literatures Database (CBM), Chinese Scientific Journal Database (VIP), Wanfang Database, from inception to October 2020. The main outcomes will contain: fasting blood glucose (FPG), 2 hours postprandial blood glucose (2hPG), glycosylated hemoglobin (HbA1c), serum calcium, bone mineral density (BMD), TCM syndrome integral, and the additional outcomes will consist of visual analog scale (VAS), and adverse events. Two reviewers will independently carry out literature search, data selection and synthesis, and literature quality assessment. In case of any dispute, it will be settled by group discussion. Assessment of risk of bias, reporting bias, and data synthesis would be performed with Review Manager software (Rev-Man 5.3).

RESULT

This study will collate and summarize the various current evidences of TCM BSJPHX principle for T2DOP.

CONCLUSION

This study will offer convincing evidence for judging the efficacy and safety of TCM BSJPHX principle for T2DOP.

PROSPERO REGISTRATION NUMBER

CRD42020218877.

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Background

Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown.

Methods

Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE.

Results

We found there existed a significant positive genetic correlation (r_g = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway.

Conclusion

This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.

Helicobacter pylori Related Diseases and Osteoporotic Fractures (Narrative Review)

Osteoporosis (OP) and osteoporotic fractures (OFs) are common multifactorial and heterogenic disorders of increasing incidence. Helicobacter pylori (H.p.) colonizes the stomach approximately in half of the world's population, causes gastroduodenal diseases and is prevalent in numerous extra-digestive diseases known to be associated with OP/OF. The studies regarding relationship between H.p. infection (HPI) and OP/OFs are inconsistent. The current review summarizes the relevant literature on the potential role of HPI in OP, falls and OFs and highlights the reasons for controversies in the publications. In the first section, after a brief overview of HPI biological features, we analyze the studies evaluating the association of HPI and bone status. The second part includes data on the prevalence of OP/OFs in HPI-induced gastroduodenal diseases (peptic ulcer, chronic/atrophic gastritis and cancer) and the effects of acid-suppressive drugs. In the next section, we discuss the possible contribution of HPI-associated extra-digestive diseases and medications to OP/OF, focusing on conditions affecting both bone homeostasis and predisposing to falls. In the last section, we describe clinical implications of accumulated data on HPI as a co-factor of OP/OF and present a feasible five-step algorithm for OP/OF risk assessment and management in regard to HPI, emphasizing the importance of an integrative (but differentiated) holistic approach. Increased awareness about the consequences of HPI linked to OP/OF can aid early detection and management. Further research on the HPI-OP/OF relationship is needed to close current knowledge gaps and improve clinical management of both OP/OF and HPI-related disorders.

Genetic Pleiotropy of Bone-Related Phenotypes: Insights from Osteoporosis

PURPOSE OF REVIEW

We summarize recent evidence on the shared genetics within and outside the musculoskeletal system (mostly related to bone density and osteoporosis).

RECENT FINDINGS

Osteoporosis is determined by an interplay between multiple genetic and environmental factors. Significant progress has been made regarding its genetic background revealing a number of robustly validated loci and respective pathways. However, pleiotropic factors affecting bone and other tissues are not well understood. The analytical methods proposed to test for potential associations between genetic variants and multiple phenotypes can be applied to bone-related data. A number of recent genetic studies have shown evidence of pleiotropy between bone density and other different phenotypes (traits, conditions, or diseases), within and outside the musculoskeletal system. Power benefits of combining correlated phenotypes, as well as unbiased discovery, make these studies promising. Studies in humans are supported by evidence from animal models. Drug development and repurposing should benefit from the pleiotropic approach. We believe that future studies should take into account shared genetics between the bone and related traits.

Network and pathway-based analyses of genes associated with osteoporosis

Osteoporosis (OP) is a disease characterized by bone mass loss, bone microstructure damage, increased bone fragility, and easy fracture. The molecular mechanism underlying OP remains unclear.In this study, we identified 217 genes associated with OP, and formed a gene set [OP-related genes gene set (OPgset)].The highly enriched GOs and pathways showed OPgset genes were significantly involved in multiple biological processes (skeletal system development, ossification, and osteoblast differentiation), and several OP-related pathways (Wnt signaling pathway, osteoclast differentiation, steroid hormone biosynthesis, and adipocytokine signaling pathway). Besides, pathway crosstalk analysis indicated three major modules, with first module consisted of pathways mainly involved in bone development-related signaling pathways, second module in Wnt-related signaling pathway and third module in metabolic pathways. Further, we calculated degree centrality of a node and selected ten key genes/proteins, including TGFB1, IL6, WNT3A, TNF, PTH, TP53, WNT1, IGF1, IL10, and SERPINE1. We analyze the K-core and construct three k-core sub-networks of OPgset genes.In summary, we for the first time explored the molecular mechanism underlying OP via network- and pathway-based methods, results from our study will improve our understanding of the pathogenesis of OP. In addition, these methods performed in this study can be used to explore pathogenesis and genes related to a specific disease.