Pub Date : 2016-10-26DOI: 10.1161/CIRCGENETICS.116.001457
T. Varga, Alexandra H Winters, K. Jablonski, E. Horton, Prajakta Khare-Ranade, W. Knowler, S. Marcovina, F. Renström, K. Watson, R. Goldberg, J. Florez, T. Pollin, P. Franks
Background—We assessed whether 234 established dyslipidemia-associated loci modify the effects of metformin treatment and lifestyle intervention (versus placebo control) on lipid and lipid subfraction levels in the Diabetes Prevention Program randomized controlled trial. Methods and Results—We tested gene treatment interactions in relation to baseline-adjusted follow-up blood lipid concentrations (high-density lipoprotein [HDL] and low-density lipoprotein-cholesterol, total cholesterol, and triglycerides) and lipoprotein subfraction particle concentrations and size in 2993 participants with pre–diabetes. Of the previously reported single-nucleotide polymorphism associations, 32.5% replicated at P<0.05 with baseline lipid traits. Trait-specific genetic risk scores were robustly associated (3×10–4>P>1.1×10–16) with their respective baseline traits for all but 2 traits. Lifestyle modified the effect of the genetic risk score for large HDL particle numbers, such that each risk allele of the genetic risk scores was associated with lower concentrations of large HDL particles at follow-up in the lifestyle arm (&bgr;=−0.11 µmol/L per genetic risk scores risk allele; 95% confidence interval, −0.188 to −0.033; P=5×10–3; Pinteraction=1×10–3 for lifestyle versus placebo), but not in the metformin or placebo arms (P>0.05). In the lifestyle arm, participants with high genetic risk had more favorable or similar trait levels at 1-year compared with participants at lower genetic risk at baseline for 17 of the 20 traits. Conclusions—Improvements in large HDL particle concentrations conferred by lifestyle may be diminished by genetic factors. Lifestyle intervention, however, was successful in offsetting unfavorable genetic loading for most lipid traits. Clinical Trial Registration—URL: https://www.clinicaltrials.gov. Unique Identifier: NCT00004992.
{"title":"Comprehensive Analysis of Established Dyslipidemia-Associated Loci in the Diabetes Prevention Program","authors":"T. Varga, Alexandra H Winters, K. Jablonski, E. Horton, Prajakta Khare-Ranade, W. Knowler, S. Marcovina, F. Renström, K. Watson, R. Goldberg, J. Florez, T. Pollin, P. Franks","doi":"10.1161/CIRCGENETICS.116.001457","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.116.001457","url":null,"abstract":"Background—We assessed whether 234 established dyslipidemia-associated loci modify the effects of metformin treatment and lifestyle intervention (versus placebo control) on lipid and lipid subfraction levels in the Diabetes Prevention Program randomized controlled trial. Methods and Results—We tested gene treatment interactions in relation to baseline-adjusted follow-up blood lipid concentrations (high-density lipoprotein [HDL] and low-density lipoprotein-cholesterol, total cholesterol, and triglycerides) and lipoprotein subfraction particle concentrations and size in 2993 participants with pre–diabetes. Of the previously reported single-nucleotide polymorphism associations, 32.5% replicated at P<0.05 with baseline lipid traits. Trait-specific genetic risk scores were robustly associated (3×10–4>P>1.1×10–16) with their respective baseline traits for all but 2 traits. Lifestyle modified the effect of the genetic risk score for large HDL particle numbers, such that each risk allele of the genetic risk scores was associated with lower concentrations of large HDL particles at follow-up in the lifestyle arm (&bgr;=−0.11 µmol/L per genetic risk scores risk allele; 95% confidence interval, −0.188 to −0.033; P=5×10–3; Pinteraction=1×10–3 for lifestyle versus placebo), but not in the metformin or placebo arms (P>0.05). In the lifestyle arm, participants with high genetic risk had more favorable or similar trait levels at 1-year compared with participants at lower genetic risk at baseline for 17 of the 20 traits. Conclusions—Improvements in large HDL particle concentrations conferred by lifestyle may be diminished by genetic factors. Lifestyle intervention, however, was successful in offsetting unfavorable genetic loading for most lipid traits. Clinical Trial Registration—URL: https://www.clinicaltrials.gov. Unique Identifier: NCT00004992.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.116.001457","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-26DOI: 10.1161/CIRCGENETICS.116.001444
M. Lacruz, A. Kluttig, D. Tiller, D. Medenwald, I. Giegling, D. Rujescu, C. Prehn, J. Adamski, S. Frantz, K. Greiser, R. Emeny, G. Kastenmüller, J. Haerting
Background—The effects of lifestyle risk factors considered collectively on the human metabolism are to date unknown. We aim to investigate the association of these risk factors with metabolites and their changes during 4 years. Methods and Results—One hundred and sixty-three metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45 to 83 years at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lysophosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (body mass index [BMI], alcohol consumption, smoking, diet, and exercise). Multilevel or simple linear regression modeling adjusted for relevant covariates was used for the evaluation of cross-sectional or longitudinal associations, respectively; multiple testing correction was based on false discovery rate. BMI, alcohol consumption, and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines, and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines. Conclusions—This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.
{"title":"Cardiovascular Risk Factors Associated With Blood Metabolite Concentrations and Their Alterations During a 4-Year Period in a Population-Based Cohort","authors":"M. Lacruz, A. Kluttig, D. Tiller, D. Medenwald, I. Giegling, D. Rujescu, C. Prehn, J. Adamski, S. Frantz, K. Greiser, R. Emeny, G. Kastenmüller, J. Haerting","doi":"10.1161/CIRCGENETICS.116.001444","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.116.001444","url":null,"abstract":"Background—The effects of lifestyle risk factors considered collectively on the human metabolism are to date unknown. We aim to investigate the association of these risk factors with metabolites and their changes during 4 years. Methods and Results—One hundred and sixty-three metabolites were measured in serum samples with the AbsoluteIDQ kit p150 (Biocrates) following a targeted metabolomics approach, in a population-based cohort of 1030 individuals, aged 45 to 83 years at baseline. We evaluated associations between metabolite concentrations (28 acylcarnitines, 14 amino acids, 9 lysophosphocholines, 72 phosphocholines, 10 sphingomyelins and sum of hexoses) and 5 lifestyle risk factors (body mass index [BMI], alcohol consumption, smoking, diet, and exercise). Multilevel or simple linear regression modeling adjusted for relevant covariates was used for the evaluation of cross-sectional or longitudinal associations, respectively; multiple testing correction was based on false discovery rate. BMI, alcohol consumption, and smoking were associated with lipid metabolism (reduced lyso- and acyl-alkyl-phosphatidylcholines and increased diacylphosphatidylcholines concentrations). Smoking showed positive associations with acylcarnitines, and BMI correlated inversely with nonessential amino acids. Fewer metabolites showed relative changes that were associated with baseline risk factors: increases in 5 different acyl-alkyl phosphatidylcholines were associated with lower alcohol consumption and BMI and with a healthier diet. Increased levels of tyrosine were associated with BMI. Sex-specific effects of smoking and BMI were found specifically related to acylcarnitine metabolism: in women higher BMI and in men more pack-years were associated with increases in acylcarnitines. Conclusions—This study showed sex-specific effects of lifestyle risks factors on human metabolism and highlighted their long-term metabolic consequences.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.116.001444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-25DOI: 10.1161/CIRCGENETICS.116.001522
C. Iribarren, Meng Lu, E. Jorgenson, Manuel Martínez, C. Lluís-Ganella, I. Subirana, E. Salas, R. Elosúa
Background—We evaluated whether including multilocus genetic risk scores (GRSs) into the Framingham Risk Equation improves the predictive capacity, discrimination, and reclassification of asymptomatic individuals with respect to coronary heart disease (CHD) risk. Methods and Results—We performed a cohort study among 51 954 European-ancestry members of a Northern California integrated healthcare system (67% female; mean age 59) free of CHD at baseline (2007–2008). Four GRSs were constructed using between 8 and 51 previously identified genetic variants. After a mean (±SD) follow-up of 5.9 (±1.5) years, 1864 incident CHD events were documented. All GRSs were linearly associated with CHD in a model adjusted by individual risk factors: hazard ratio (95% confidence interval) per SD unit: 1.21 (1.15–1.26) for GRS_8, 1.20 (1.15–1.26) for GRS_12, 1.23 (1.17–1.28) for GRS_36, and 1.23 (1.17–1.28) for GRS_51. Inclusion of the GRSs improved the C statistic (&Dgr;C statistic =0.008 for GRS_8 and GRS_36; 0.007 for GRS_12; and 0.009 for GRS_51; all P<0.001). The net reclassification improvement was 5% for GRS_8, GRS_12, and GRS_36 and 4% for GRS_51 in the entire cohort and was (after correcting for bias) 9% for GRS_8 and GRS_12 and 7% for GRS_36 and GRS_51 when analyzing those classified as intermediate Framingham risk (10%–20%). The number required to treat to prevent 1 CHD after selectively treating with statins up-reclassified subjects on the basis of genetic information was 36 for GRS_8 and GRS_12, 41 for GRS_36, and 43 for GRS_51. Conclusions—Our results demonstrate significant and clinically relevant incremental discriminative/predictive capability of 4 multilocus GRSs for incident CHD among subjects of European ancestry.
{"title":"Clinical Utility of Multimarker Genetic Risk Scores for Prediction of Incident Coronary Heart Disease: A Cohort Study Among Over 51 000 Individuals of European Ancestry","authors":"C. Iribarren, Meng Lu, E. Jorgenson, Manuel Martínez, C. Lluís-Ganella, I. Subirana, E. Salas, R. Elosúa","doi":"10.1161/CIRCGENETICS.116.001522","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.116.001522","url":null,"abstract":"Background—We evaluated whether including multilocus genetic risk scores (GRSs) into the Framingham Risk Equation improves the predictive capacity, discrimination, and reclassification of asymptomatic individuals with respect to coronary heart disease (CHD) risk. Methods and Results—We performed a cohort study among 51 954 European-ancestry members of a Northern California integrated healthcare system (67% female; mean age 59) free of CHD at baseline (2007–2008). Four GRSs were constructed using between 8 and 51 previously identified genetic variants. After a mean (±SD) follow-up of 5.9 (±1.5) years, 1864 incident CHD events were documented. All GRSs were linearly associated with CHD in a model adjusted by individual risk factors: hazard ratio (95% confidence interval) per SD unit: 1.21 (1.15–1.26) for GRS_8, 1.20 (1.15–1.26) for GRS_12, 1.23 (1.17–1.28) for GRS_36, and 1.23 (1.17–1.28) for GRS_51. Inclusion of the GRSs improved the C statistic (&Dgr;C statistic =0.008 for GRS_8 and GRS_36; 0.007 for GRS_12; and 0.009 for GRS_51; all P<0.001). The net reclassification improvement was 5% for GRS_8, GRS_12, and GRS_36 and 4% for GRS_51 in the entire cohort and was (after correcting for bias) 9% for GRS_8 and GRS_12 and 7% for GRS_36 and GRS_51 when analyzing those classified as intermediate Framingham risk (10%–20%). The number required to treat to prevent 1 CHD after selectively treating with statins up-reclassified subjects on the basis of genetic information was 36 for GRS_8 and GRS_12, 41 for GRS_36, and 43 for GRS_51. Conclusions—Our results demonstrate significant and clinically relevant incremental discriminative/predictive capability of 4 multilocus GRSs for incident CHD among subjects of European ancestry.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.116.001522","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-25DOI: 10.1161/CIRCGENETICS.116.001515
Stephanie M. Cossette, Vijesh J. Bhute, Xiaoping Bao, Leanne M. Harmann, Mark A Horswill, I. Sinha, Adam J. Gastonguay, S. Pooya, Michelle Bordas, Suresh N. Kumar, S. Mirza, S. Palecek, J. Strande, R. Ramchandran
Background—Cardiac metabolism is critical for the functioning of the heart, and disturbance in this homeostasis is likely to influence cardiac disorders or cardiomyopathy. Our laboratory has previously shown that SNRK (sucrose nonfermenting related kinase) enzyme, which belongs to the AMPK (adenosine monophosphate–activated kinase) family, was essential for cardiac metabolism in mammals. Snrk global homozygous knockout (KO) mice die at postnatal day 0, and conditional deletion of Snrk in cardiomyocytes (Snrk cmcKO) leads to cardiac failure and death by 8 to 10 months. Methods and Results—We performed additional cardiac functional studies using echocardiography and identified further cardiac functional deficits in Snrk cmcKO mice. Nuclear magnetic resonance-based metabolomics analysis identified key metabolic pathway deficits in SNRK knockdown cardiomyocytes in vitro. Specifically, metabolites involved in lipid metabolism and oxidative phosphorylation are altered, and perturbations in these pathways can result in cardiac function deficits and heart failure. A phosphopeptide-based proteomic screen identified ROCK (Rho-associated kinase) as a putative substrate for SNRK, and mass spec-based fragment analysis confirmed key amino acid residues on ROCK that are phosphorylated by SNRK. Western blot analysis on heart lysates from Snrk cmcKO adult mice and SNRK knockdown cardiomyocytes showed increased ROCK activity. In addition, in vivo inhibition of ROCK partially rescued the in vivo Snrk cmcKO cardiac function deficits. Conclusions—Collectively, our data suggest that SNRK in cardiomyocytes is responsible for maintaining cardiac metabolic homeostasis, which is mediated in part by ROCK, and alteration of this homeostasis influences cardiac function in the adult heart.
{"title":"Sucrose Nonfermenting-Related Kinase Enzyme–Mediated Rho-Associated Kinase Signaling is Responsible for Cardiac Function","authors":"Stephanie M. Cossette, Vijesh J. Bhute, Xiaoping Bao, Leanne M. Harmann, Mark A Horswill, I. Sinha, Adam J. Gastonguay, S. Pooya, Michelle Bordas, Suresh N. Kumar, S. Mirza, S. Palecek, J. Strande, R. Ramchandran","doi":"10.1161/CIRCGENETICS.116.001515","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.116.001515","url":null,"abstract":"Background—Cardiac metabolism is critical for the functioning of the heart, and disturbance in this homeostasis is likely to influence cardiac disorders or cardiomyopathy. Our laboratory has previously shown that SNRK (sucrose nonfermenting related kinase) enzyme, which belongs to the AMPK (adenosine monophosphate–activated kinase) family, was essential for cardiac metabolism in mammals. Snrk global homozygous knockout (KO) mice die at postnatal day 0, and conditional deletion of Snrk in cardiomyocytes (Snrk cmcKO) leads to cardiac failure and death by 8 to 10 months. Methods and Results—We performed additional cardiac functional studies using echocardiography and identified further cardiac functional deficits in Snrk cmcKO mice. Nuclear magnetic resonance-based metabolomics analysis identified key metabolic pathway deficits in SNRK knockdown cardiomyocytes in vitro. Specifically, metabolites involved in lipid metabolism and oxidative phosphorylation are altered, and perturbations in these pathways can result in cardiac function deficits and heart failure. A phosphopeptide-based proteomic screen identified ROCK (Rho-associated kinase) as a putative substrate for SNRK, and mass spec-based fragment analysis confirmed key amino acid residues on ROCK that are phosphorylated by SNRK. Western blot analysis on heart lysates from Snrk cmcKO adult mice and SNRK knockdown cardiomyocytes showed increased ROCK activity. In addition, in vivo inhibition of ROCK partially rescued the in vivo Snrk cmcKO cardiac function deficits. Conclusions—Collectively, our data suggest that SNRK in cardiomyocytes is responsible for maintaining cardiac metabolic homeostasis, which is mediated in part by ROCK, and alteration of this homeostasis influences cardiac function in the adult heart.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.116.001515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1161/HCG.0000000000000031
S. Mital, K. Musunuru, V. Garg, M. Russell, D. Lanfear, Rajat M. Gupta, K. Hickey, M. Ackerman, M. Perez, D. Roden, Daniel Woo, C. Fox, S. Ware
Advances in genomics are enhancing our understanding of the genetic basis of cardiovascular diseases, both congenital and acquired, and stroke. These advances include finding genes that cause or increase the risk for childhood and adult-onset diseases, finding genes that influence how patients respond to medications, and the development of genetics-guided therapies for diseases. However, the ability of cardiovascular and stroke clinicians to fully understand and apply this knowledge to the care of their patients has lagged. This statement addresses what the specialist caring for patients with cardiovascular diseases and stroke should know about genetics; how they can gain this knowledge; how they can keep up-to-date with advances in genetics, genomics, and pharmacogenetics; and how they can apply this knowledge to improve the care of patients and families with cardiovascular diseases and stroke.
{"title":"Enhancing Literacy in Cardiovascular Genetics: A Scientific Statement From the American Heart Association","authors":"S. Mital, K. Musunuru, V. Garg, M. Russell, D. Lanfear, Rajat M. Gupta, K. Hickey, M. Ackerman, M. Perez, D. Roden, Daniel Woo, C. Fox, S. Ware","doi":"10.1161/HCG.0000000000000031","DOIUrl":"https://doi.org/10.1161/HCG.0000000000000031","url":null,"abstract":"Advances in genomics are enhancing our understanding of the genetic basis of cardiovascular diseases, both congenital and acquired, and stroke. These advances include finding genes that cause or increase the risk for childhood and adult-onset diseases, finding genes that influence how patients respond to medications, and the development of genetics-guided therapies for diseases. However, the ability of cardiovascular and stroke clinicians to fully understand and apply this knowledge to the care of their patients has lagged. This statement addresses what the specialist caring for patients with cardiovascular diseases and stroke should know about genetics; how they can gain this knowledge; how they can keep up-to-date with advances in genetics, genomics, and pharmacogenetics; and how they can apply this knowledge to improve the care of patients and families with cardiovascular diseases and stroke.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/HCG.0000000000000031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64429201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01Epub Date: 2016-09-20DOI: 10.1161/CIRCGENETICS.116.001506
Roby Joehanes, Allan C Just, Riccardo E Marioni, Luke C Pilling, Lindsay M Reynolds, Pooja R Mandaviya, Weihua Guan, Tao Xu, Cathy E Elks, Stella Aslibekyan, Hortensia Moreno-Macias, Jennifer A Smith, Jennifer A Brody, Radhika Dhingra, Paul Yousefi, James S Pankow, Sonja Kunze, Sonia H Shah, Allan F McRae, Kurt Lohman, Jin Sha, Devin M Absher, Luigi Ferrucci, Wei Zhao, Ellen W Demerath, Jan Bressler, Megan L Grove, Tianxiao Huan, Chunyu Liu, Michael M Mendelson, Chen Yao, Douglas P Kiel, Annette Peters, Rui Wang-Sattler, Peter M Visscher, Naomi R Wray, John M Starr, Jingzhong Ding, Carlos J Rodriguez, Nicholas J Wareham, Marguerite R Irvin, Degui Zhi, Myrto Barrdahl, Paolo Vineis, Srikant Ambatipudi, André G Uitterlinden, Albert Hofman, Joel Schwartz, Elena Colicino, Lifang Hou, Pantel S Vokonas, Dena G Hernandez, Andrew B Singleton, Stefania Bandinelli, Stephen T Turner, Erin B Ware, Alicia K Smith, Torsten Klengel, Elisabeth B Binder, Bruce M Psaty, Kent D Taylor, Sina A Gharib, Brenton R Swenson, Liming Liang, Dawn L DeMeo, George T O'Connor, Zdenko Herceg, Kerry J Ressler, Karen N Conneely, Nona Sotoodehnia, Sharon L R Kardia, David Melzer, Andrea A Baccarelli, Joyce B J van Meurs, Isabelle Romieu, Donna K Arnett, Ken K Ong, Yongmei Liu, Melanie Waldenberger, Ian J Deary, Myriam Fornage, Daniel Levy, Stephanie J London
Background: DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders.
Methods and results: To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10-7 (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10-7 (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs.
Conclusions: Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.
{"title":"Epigenetic Signatures of Cigarette Smoking.","authors":"Roby Joehanes, Allan C Just, Riccardo E Marioni, Luke C Pilling, Lindsay M Reynolds, Pooja R Mandaviya, Weihua Guan, Tao Xu, Cathy E Elks, Stella Aslibekyan, Hortensia Moreno-Macias, Jennifer A Smith, Jennifer A Brody, Radhika Dhingra, Paul Yousefi, James S Pankow, Sonja Kunze, Sonia H Shah, Allan F McRae, Kurt Lohman, Jin Sha, Devin M Absher, Luigi Ferrucci, Wei Zhao, Ellen W Demerath, Jan Bressler, Megan L Grove, Tianxiao Huan, Chunyu Liu, Michael M Mendelson, Chen Yao, Douglas P Kiel, Annette Peters, Rui Wang-Sattler, Peter M Visscher, Naomi R Wray, John M Starr, Jingzhong Ding, Carlos J Rodriguez, Nicholas J Wareham, Marguerite R Irvin, Degui Zhi, Myrto Barrdahl, Paolo Vineis, Srikant Ambatipudi, André G Uitterlinden, Albert Hofman, Joel Schwartz, Elena Colicino, Lifang Hou, Pantel S Vokonas, Dena G Hernandez, Andrew B Singleton, Stefania Bandinelli, Stephen T Turner, Erin B Ware, Alicia K Smith, Torsten Klengel, Elisabeth B Binder, Bruce M Psaty, Kent D Taylor, Sina A Gharib, Brenton R Swenson, Liming Liang, Dawn L DeMeo, George T O'Connor, Zdenko Herceg, Kerry J Ressler, Karen N Conneely, Nona Sotoodehnia, Sharon L R Kardia, David Melzer, Andrea A Baccarelli, Joyce B J van Meurs, Isabelle Romieu, Donna K Arnett, Ken K Ong, Yongmei Liu, Melanie Waldenberger, Ian J Deary, Myriam Fornage, Daniel Levy, Stephanie J London","doi":"10.1161/CIRCGENETICS.116.001506","DOIUrl":"10.1161/CIRCGENETICS.116.001506","url":null,"abstract":"<p><strong>Background: </strong>DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders.</p><p><strong>Methods and results: </strong>To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15 907 blood-derived DNA samples from participants in 16 cohorts (including 2433 current, 6518 former, and 6956 never smokers). Comparing current versus never smokers, 2623 cytosine-phosphate-guanine sites (CpGs), annotated to 1405 genes, were statistically significantly differentially methylated at Bonferroni threshold of P<1×10<sup>-7</sup> (18 760 CpGs at false discovery rate <0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases, and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant P<1×10<sup>-7</sup> (2623 CpGs at false discovery rate <0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs.</p><p><strong>Conclusions: </strong>Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biological effects of smoking and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.</p>","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5267325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-10-01DOI: 10.1161/CIRCGENETICS.116.001402
Xiaoping Yang, A. Sethi, L. Yanek, C. Knapper, B. Nordestgaard, A. Tybjærg‐Hansen, D. Becker, R. Mathias, A. Remaley, L. Becker
BACKGROUND SR-B1 (scavenger receptor class B type 1), encoded by the gene SCARB1, is a lipoprotein receptor that binds both high-density lipoprotein (HDL) and low-density lipoprotein. We reported that SR-B1 is also a receptor for lipoprotein (a) (Lp(a)), mediating cellular uptake of Lp(a) in vitro and promoting clearance of Lp(a) in vivo. Although genetic variants in SCARB1 are associated with variations in HDL level, no SCARB1 variants affecting Lp(a) have been reported. METHODS AND RESULTS In an index subject with high levels of HDL cholesterol and Lp(a), SCARB1 was sequenced and demonstrated a missense mutation resulting in an S129L substitution in exon 3. To follow up, 2 cohorts (GeneSTAR, the family-based Genetic Study of Atherosclerosis Risk [n=543], and CCHS, the population-based Copenhagen City Heart Study [n=5835]) were screened for combined HDL cholesterol and Lp(a) elevations. Subjects with the extreme phenotype (HDL >80 mg/dL and Lp(a) >100 nmol/L in GeneSTAR, n=8, and >100 mg/dL in CCHS, n=9) underwent sequencing of SCARB1 exons; 15 of 18 from the combined population demonstrated genetic variants, including rare or uncommon missense or splice site mutations in 9 and homozygous synonymous variants in 6. Functional studies with 4 of the SCARB1 variants (c.386C>T, c.631-14T>G, c.4G>A, and c.631-53mC>T & c.726+55mCG>CA) showed decreased receptor function in vitro. CONCLUSIONS Human SCARB1 gene variants are associated with a new lipid phenotype, characterized by high levels of both HDL cholesterol and Lp(a). SCARB1 exonic variants often result in diminished function of translated SR-B1 via reduced binding/intracellular transport of Lp(a).
{"title":"SCARB1 Gene Variants Are Associated With the Phenotype of Combined High High-Density Lipoprotein Cholesterol and High Lipoprotein (a).","authors":"Xiaoping Yang, A. Sethi, L. Yanek, C. Knapper, B. Nordestgaard, A. Tybjærg‐Hansen, D. Becker, R. Mathias, A. Remaley, L. Becker","doi":"10.1161/CIRCGENETICS.116.001402","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.116.001402","url":null,"abstract":"BACKGROUND\u0000SR-B1 (scavenger receptor class B type 1), encoded by the gene SCARB1, is a lipoprotein receptor that binds both high-density lipoprotein (HDL) and low-density lipoprotein. We reported that SR-B1 is also a receptor for lipoprotein (a) (Lp(a)), mediating cellular uptake of Lp(a) in vitro and promoting clearance of Lp(a) in vivo. Although genetic variants in SCARB1 are associated with variations in HDL level, no SCARB1 variants affecting Lp(a) have been reported.\u0000\u0000\u0000METHODS AND RESULTS\u0000In an index subject with high levels of HDL cholesterol and Lp(a), SCARB1 was sequenced and demonstrated a missense mutation resulting in an S129L substitution in exon 3. To follow up, 2 cohorts (GeneSTAR, the family-based Genetic Study of Atherosclerosis Risk [n=543], and CCHS, the population-based Copenhagen City Heart Study [n=5835]) were screened for combined HDL cholesterol and Lp(a) elevations. Subjects with the extreme phenotype (HDL >80 mg/dL and Lp(a) >100 nmol/L in GeneSTAR, n=8, and >100 mg/dL in CCHS, n=9) underwent sequencing of SCARB1 exons; 15 of 18 from the combined population demonstrated genetic variants, including rare or uncommon missense or splice site mutations in 9 and homozygous synonymous variants in 6. Functional studies with 4 of the SCARB1 variants (c.386C>T, c.631-14T>G, c.4G>A, and c.631-53mC>T & c.726+55mCG>CA) showed decreased receptor function in vitro.\u0000\u0000\u0000CONCLUSIONS\u0000Human SCARB1 gene variants are associated with a new lipid phenotype, characterized by high levels of both HDL cholesterol and Lp(a). SCARB1 exonic variants often result in diminished function of translated SR-B1 via reduced binding/intracellular transport of Lp(a).","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.116.001402","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-01DOI: 10.1161/CIRCGENETICS.115.001271
Satu Vaara, E. Tikkanen, Olavi Parkkonen, M. Lokki, S. Ripatti, M. Perola, M. Nieminen, J. Sinisalo
Background—Several clinical risk estimation tools have established their role in the prediction of recurrence of acute coronary syndrome (ACS), but the value of genetic risk scores (GRSs) remains unclear. We examined how well 2 different GRSs estimate recurrent ACS and whether clinical factors are associated with GRSs. Methods and Results—A cohort of 2090 consecutive patients with ACS who underwent coronary angiography between July 2006 and March 2008 in a single tertiary center was genotyped and prospectively followed up for a median of 5.5 years. We formed 2 partially overlapping GRSs: GRS47 of 47 single-nucleotide polymorphisms with previously reported significant association with coronary artery disease and GRS153 of 153 single-nucleotide polymorphisms with significant or suggestive association with coronary artery disease. GRS47 showed association with recurrent ACS independent of clinical factors (P=0.037; hazard ratio, 1.17; 95% confidence interval, 1.01–1.36). GRS153 had no association with either recurrent ACS or composite of recurrent ACS or death. Also, GRS47 was associated inversely with smoking and ST-segment–elevation myocardial infarction (P=0.004; odds ratio, 0.22; 95% confidence interval, 0.08–0.62 and P=0.041; odds ratio, 0.36; 95% confidence interval, 0.13–0.96, respectively). Conclusions—GRSs combined of 47 known coronary artery disease risk single-nucleotide polymorphisms were associated with recurrent ACS after multivariable adjustments in a heterogenic ACS population for the first time. Smoking and ST-segment–elevation myocardial infarction had an inverse association with the GRSs. The significance of smoking in relation to genetic coronary artery disease predisposition may merit further evaluation in patients with ACS.
{"title":"Genetic Risk Scores Predict Recurrence of Acute Coronary Syndrome","authors":"Satu Vaara, E. Tikkanen, Olavi Parkkonen, M. Lokki, S. Ripatti, M. Perola, M. Nieminen, J. Sinisalo","doi":"10.1161/CIRCGENETICS.115.001271","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.115.001271","url":null,"abstract":"Background—Several clinical risk estimation tools have established their role in the prediction of recurrence of acute coronary syndrome (ACS), but the value of genetic risk scores (GRSs) remains unclear. We examined how well 2 different GRSs estimate recurrent ACS and whether clinical factors are associated with GRSs. Methods and Results—A cohort of 2090 consecutive patients with ACS who underwent coronary angiography between July 2006 and March 2008 in a single tertiary center was genotyped and prospectively followed up for a median of 5.5 years. We formed 2 partially overlapping GRSs: GRS47 of 47 single-nucleotide polymorphisms with previously reported significant association with coronary artery disease and GRS153 of 153 single-nucleotide polymorphisms with significant or suggestive association with coronary artery disease. GRS47 showed association with recurrent ACS independent of clinical factors (P=0.037; hazard ratio, 1.17; 95% confidence interval, 1.01–1.36). GRS153 had no association with either recurrent ACS or composite of recurrent ACS or death. Also, GRS47 was associated inversely with smoking and ST-segment–elevation myocardial infarction (P=0.004; odds ratio, 0.22; 95% confidence interval, 0.08–0.62 and P=0.041; odds ratio, 0.36; 95% confidence interval, 0.13–0.96, respectively). Conclusions—GRSs combined of 47 known coronary artery disease risk single-nucleotide polymorphisms were associated with recurrent ACS after multivariable adjustments in a heterogenic ACS population for the first time. Smoking and ST-segment–elevation myocardial infarction had an inverse association with the GRSs. The significance of smoking in relation to genetic coronary artery disease predisposition may merit further evaluation in patients with ACS.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.115.001271","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-01DOI: 10.1161/CIRCGENETICS.115.001264
Chunjiang He, Hanyang Hu, Kitchener D. Wilson, Haodi Wu, Jing Feng, Si-Yu Xia, Jared M. Churko, K. Qu, Howard Y. Chang, Joseph C. Wu
Background—The molecular regulation of heart development is regulated by cis- and trans-factors acting on the genome and epigenome. As a class of important regulatory RNAs, the role of long noncoding RNAs (lncRNAs) in human heart development is still poorly understood. Furthermore, factors that interact with lncRNAs in this process are not well characterized. Methods and Results—Using RNA sequencing, we systematically define the contrasting lncRNA expression patterns between fetal and adult hearts. We report that lncRNAs upregulated in adult versus fetal heart have different sequence features and distributions. For example, the adult heart expresses more sense lncRNAs compared with fetal heart. We also report the coexpression of lncRNAs and neighboring coding genes that have important functions in heart development. Importantly, the regulation of lncRNA expression during fetal to adult heart development seems to be due, in part, to the coordination of specific developmental epigenetic modifications, such as H3K4me1 and H3k4me3. The expression of promoter-associated lncRNAs in adult and fetal hearts also seems to be related to these epigenetic states. Finally, transcription factor–binding analysis suggests that lncRNAs are directly regulating cardiac gene expression during development. Conclusions—We provide a systematic analysis of lncRNA control of heart development that gives clues to the roles that specific lncRNAs play in fetal and adult hearts.
{"title":"Systematic Characterization of Long Noncoding RNAs Reveals the Contrasting Coordination of Cis- and Trans-Molecular Regulation in Human Fetal and Adult Hearts","authors":"Chunjiang He, Hanyang Hu, Kitchener D. Wilson, Haodi Wu, Jing Feng, Si-Yu Xia, Jared M. Churko, K. Qu, Howard Y. Chang, Joseph C. Wu","doi":"10.1161/CIRCGENETICS.115.001264","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.115.001264","url":null,"abstract":"Background—The molecular regulation of heart development is regulated by cis- and trans-factors acting on the genome and epigenome. As a class of important regulatory RNAs, the role of long noncoding RNAs (lncRNAs) in human heart development is still poorly understood. Furthermore, factors that interact with lncRNAs in this process are not well characterized. Methods and Results—Using RNA sequencing, we systematically define the contrasting lncRNA expression patterns between fetal and adult hearts. We report that lncRNAs upregulated in adult versus fetal heart have different sequence features and distributions. For example, the adult heart expresses more sense lncRNAs compared with fetal heart. We also report the coexpression of lncRNAs and neighboring coding genes that have important functions in heart development. Importantly, the regulation of lncRNA expression during fetal to adult heart development seems to be due, in part, to the coordination of specific developmental epigenetic modifications, such as H3K4me1 and H3k4me3. The expression of promoter-associated lncRNAs in adult and fetal hearts also seems to be related to these epigenetic states. Finally, transcription factor–binding analysis suggests that lncRNAs are directly regulating cardiac gene expression during development. Conclusions—We provide a systematic analysis of lncRNA control of heart development that gives clues to the roles that specific lncRNAs play in fetal and adult hearts.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.115.001264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2016-04-01DOI: 10.1161/CIRCGENETICS.115.001246
Y. Klimentidis, Amit Arora
Background—Several studies suggest that some triglyceride-associated single-nucleotide polymorphisms (SNPs) have pleiotropic and opposite effects on glycemic traits. This potentially implicates them in pathways such as de novo lipogenesis, which is presumably upregulated in the context of insulin resistance. We therefore tested whether the association of triglyceride-associated SNPs with triglyceride levels differs according to one’s level of insulin resistance. Methods and Results—In 3 cohort studies (combined n=12 487), we tested the interaction of established triglyceride-associated SNPs (individually and collectively) with several traits related to insulin resistance, on triglyceride levels. We also tested the interaction of triglyceride SNPs with fasting insulin–associated SNPs, individually and collectively, on triglyceride levels. We find significant interactions of a weighted genetic risk score for triglycerides with insulin resistance on triglyceride levels (Pinteraction=2.73×10−11 and Pinteraction=2.48×10–11 for fasting insulin and homeostasis model assessment of insulin resistance, respectively). The association of the triglyceride genetic risk score with triglyceride levels is >60% stronger among those in the highest tertile of homeostasis model assessment of insulin resistance compared with those in the lowest tertile. Individual SNPs contributing to this trend include those in/near GCKR, CILP2, and IRS1, whereas PIGV-NROB2 and LRPAP1 display an opposite trend of interaction. In the pooled data set, we also identify a SNP–by–SNP interaction involving a triglyceride-associated SNP, rs4722551 near MIR148A, with a fasting insulin–associated SNP, rs4865796 in ARL15 (Pinteraction=4.1×10−5). Conclusions—Our findings may thus provide genetic evidence for the upregulation of triglyceride levels in insulin-resistant individuals, in addition to identifying specific genetic loci and a SNP–by–SNP interaction implicated in this process.
{"title":"Interaction of Insulin Resistance and Related Genetic Variants With Triglyceride-Associated Genetic Variants","authors":"Y. Klimentidis, Amit Arora","doi":"10.1161/CIRCGENETICS.115.001246","DOIUrl":"https://doi.org/10.1161/CIRCGENETICS.115.001246","url":null,"abstract":"Background—Several studies suggest that some triglyceride-associated single-nucleotide polymorphisms (SNPs) have pleiotropic and opposite effects on glycemic traits. This potentially implicates them in pathways such as de novo lipogenesis, which is presumably upregulated in the context of insulin resistance. We therefore tested whether the association of triglyceride-associated SNPs with triglyceride levels differs according to one’s level of insulin resistance. Methods and Results—In 3 cohort studies (combined n=12 487), we tested the interaction of established triglyceride-associated SNPs (individually and collectively) with several traits related to insulin resistance, on triglyceride levels. We also tested the interaction of triglyceride SNPs with fasting insulin–associated SNPs, individually and collectively, on triglyceride levels. We find significant interactions of a weighted genetic risk score for triglycerides with insulin resistance on triglyceride levels (Pinteraction=2.73×10−11 and Pinteraction=2.48×10–11 for fasting insulin and homeostasis model assessment of insulin resistance, respectively). The association of the triglyceride genetic risk score with triglyceride levels is >60% stronger among those in the highest tertile of homeostasis model assessment of insulin resistance compared with those in the lowest tertile. Individual SNPs contributing to this trend include those in/near GCKR, CILP2, and IRS1, whereas PIGV-NROB2 and LRPAP1 display an opposite trend of interaction. In the pooled data set, we also identify a SNP–by–SNP interaction involving a triglyceride-associated SNP, rs4722551 near MIR148A, with a fasting insulin–associated SNP, rs4865796 in ARL15 (Pinteraction=4.1×10−5). Conclusions—Our findings may thus provide genetic evidence for the upregulation of triglyceride levels in insulin-resistant individuals, in addition to identifying specific genetic loci and a SNP–by–SNP interaction implicated in this process.","PeriodicalId":48940,"journal":{"name":"Circulation-Cardiovascular Genetics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1161/CIRCGENETICS.115.001246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64397175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：