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Genetics of aging, health, and survival: dynamic regulation of human longevity related traits.

Frontiers in genetics

Yashin AI, Wu D, Arbeeva LS, Arbeev KG, Kulminski AM, Akushevich I, Kovtun M, Culminskaya I, Stallard E, Li M, Ukraintseva SV.
PMID: 25918517
Front Genet. 2015 Apr 13;6:122. doi: 10.3389/fgene.2015.00122. eCollection 2015.

BACKGROUND: The roles of genetic factors in human longevity would be better understood if one can use more efficient methods in genetic analyses and investigate pleiotropic effects of genetic variants on aging and health related traits.DATA AND METHODS: We...

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Yashin AI, Wu D, Arbeeva LS, et al. Genetics of aging, health, and survival: dynamic regulation of human longevity related traits. Front Genet. 2015;6:122doi: 10.3389/fgene.2015.00122.
Yashin, A. I., Wu, D., Arbeeva, L. S., Arbeev, K. G., Kulminski, A. M., Akushevich, I., Kovtun, M., Culminskaya, I., Stallard, E., Li, M., & Ukraintseva, S. V. (2015). Genetics of aging, health, and survival: dynamic regulation of human longevity related traits. Frontiers in genetics, 6122. https://doi.org/10.3389/fgene.2015.00122
Yashin, Anatoliy I, et al. "Genetics of aging, health, and survival: dynamic regulation of human longevity related traits." Frontiers in genetics vol. 6 (2015): 122. doi: https://doi.org/10.3389/fgene.2015.00122
Yashin AI, Wu D, Arbeeva LS, Arbeev KG, Kulminski AM, Akushevich I, Kovtun M, Culminskaya I, Stallard E, Li M, Ukraintseva SV. Genetics of aging, health, and survival: dynamic regulation of human longevity related traits. Front Genet. 2015 Apr 13;6:122. doi: 10.3389/fgene.2015.00122. eCollection 2015. PMID: 25918517; PMCID: PMC4394697.
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A strategy for building and using a human reference pangenome.

F1000Research

Llamas B, Narzisi G, Schneider V, Audano PA, Biederstedt E, Blauvelt L, Bradbury P, Chang X, Chin CS, Fungtammasan A, Clarke WE, Cleary A, Ebler J, Eizenga J, Sibbesen JA, Markello CJ, Garrison E, Garg S, Hickey G, Lazo GR, Lin MF, Mahmoud M, Marschall T, Minkin I, Monlong J, Musunuri RL, Sagayaradj S, Novak AM, Rautiainen M, Regier A, Sedlazeck FJ, Siren J, Souilmi Y, Wagner J, Wrightsman T, Yokoyama TT, Zeng Q, Zook JM, Paten B, Busby B.
PMID: 34386196
F1000Res. 2019 Oct 14;8:1751. doi: 10.12688/f1000research.19630.2. eCollection 2019.

In March 2019, 45 scientists and software engineers from around the world converged at the University of California, Santa Cruz for the first pangenomics codeathon. The purpose of the meeting was to propose technical specifications and standards for a...

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Llamas B, Narzisi G, Schneider V, et al. A strategy for building and using a human reference pangenome. F1000Res. 2019;8:1751doi: 10.12688/f1000research.19630.2.
Llamas, B., Narzisi, G., Schneider, V., Audano, P. A., Biederstedt, E., Blauvelt, L., Bradbury, P., Chang, X., Chin, C. S., Fungtammasan, A., Clarke, W. E., Cleary, A., Ebler, J., Eizenga, J., Sibbesen, J. A., Markello, C. J., Garrison, E., Garg, S., Hickey, G., Lazo, G. R., Lin, M. F., Mahmoud, M., Marschall, T., Minkin, I., Monlong, J., Musunuri, R. L., Sagayaradj, S., Novak, A. M., Rautiainen, M., Regier, A., Sedlazeck, F. J., Siren, J., Souilmi, Y., Wagner, J., Wrightsman, T., Yokoyama, T. T., Zeng, Q., Zook, J. M., Paten, B., & Busby, B. (2019). A strategy for building and using a human reference pangenome. F1000Research, 81751. https://doi.org/10.12688/f1000research.19630.2
Llamas, Bastien, et al. "A strategy for building and using a human reference pangenome." F1000Research vol. 8 (2019): 1751. doi: https://doi.org/10.12688/f1000research.19630.2
Llamas B, Narzisi G, Schneider V, Audano PA, Biederstedt E, Blauvelt L, Bradbury P, Chang X, Chin CS, Fungtammasan A, Clarke WE, Cleary A, Ebler J, Eizenga J, Sibbesen JA, Markello CJ, Garrison E, Garg S, Hickey G, Lazo GR, Lin MF, Mahmoud M, Marschall T, Minkin I, Monlong J, Musunuri RL, Sagayaradj S, Novak AM, Rautiainen M, Regier A, Sedlazeck FJ, Siren J, Souilmi Y, Wagner J, Wrightsman T, Yokoyama TT, Zeng Q, Zook JM, Paten B, Busby B. A strategy for building and using a human reference pangenome. F1000Res. 2019 Oct 14;8:1751. doi: 10.12688/f1000research.19630.2. eCollection 2019. PMID: 34386196; PMCID: PMC8350888.2.
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How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data.

North American actuarial journal : NAAJ

Yashin AI, Arbeev KG, Wu D, Arbeeva L, Kulminski A, Kulminskaya I, Akushevich I, Ukraintseva SV.
PMID: 27773987
N Am Actuar J. 2016;20(3):201-232. doi: 10.1080/10920277.2016.1178588. Epub 2016 Jun 22.

BACKGROUND AND OBJECTIVE: To clarify mechanisms of genetic regulation of human aging and longevity traits, a number of genome-wide association studies (GWAS) of these traits have been performed. However, the results of these analyses did not meet expectations of...

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Yashin AI, Arbeev KG, Wu D, et al. How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data. N Am Actuar J. 2016;20(3):201-232doi: 10.1080/10920277.2016.1178588.
Yashin, A. I., Arbeev, K. G., Wu, D., Arbeeva, L., Kulminski, A., Kulminskaya, I., Akushevich, I., & Ukraintseva, S. V. (2016). How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data. North American actuarial journal : NAAJ, 20(3), 201-232. https://doi.org/10.1080/10920277.2016.1178588
Yashin, Anatoliy I, et al. "How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data." North American actuarial journal : NAAJ vol. 20,3 (2016): 201-232. doi: https://doi.org/10.1080/10920277.2016.1178588
Yashin AI, Arbeev KG, Wu D, Arbeeva L, Kulminski A, Kulminskaya I, Akushevich I, Ukraintseva SV. How Genes Modulate Patterns of Aging-Related Changes on the Way to 100: Biodemographic Models and Methods in Genetic Analyses of Longitudinal Data. N Am Actuar J. 2016;20(3):201-232. doi: 10.1080/10920277.2016.1178588. Epub 2016 Jun 22. PMID: 27773987; PMCID: PMC5070546.
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Application of Heterogeneity of Treatment Effect Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee OA.

Arthritis care & research

Coffman CJ, Arbeeva L, Schwartz TA, Callahan LF, Golightly YM, Goode AP, Huffman KM, Allen KD.
PMID: 33463020
Arthritis Care Res (Hoboken). 2021 Jan 18; doi: 10.1002/acr.24564. Epub 2021 Jan 18.

OBJECTIVE: To evaluate heterogeneity of treatment effects (HTE) in a trial of exercise-based interventions for knee osteoarthritis (OA).METHODS: Participants (n=350) were randomized to standard physical therapy (PT; n=140), Internet-Based Exercise Training (IBET; n=142), or wait list control (WL; n=68)....

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Coffman CJ, Arbeeva L, Schwartz TA, et al. Application of Heterogeneity of Treatment Effect Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee OA. Arthritis Care Res (Hoboken). 2021;doi: 10.1002/acr.24564.
Coffman, C. J., Arbeeva, L., Schwartz, T. A., Callahan, L. F., Golightly, Y. M., Goode, A. P., Huffman, K. M., & Allen, K. D. (2021). Application of Heterogeneity of Treatment Effect Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee OA. Arthritis care & research, . https://doi.org/10.1002/acr.24564
Coffman, Cynthia J, et al. "Application of Heterogeneity of Treatment Effect Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee OA." Arthritis care & research vol. (2021). doi: https://doi.org/10.1002/acr.24564
Coffman CJ, Arbeeva L, Schwartz TA, Callahan LF, Golightly YM, Goode AP, Huffman KM, Allen KD. Application of Heterogeneity of Treatment Effect Methods: Exploratory Analyses of a Trial of Exercise-Based Interventions for Knee OA. Arthritis Care Res (Hoboken). 2021 Jan 18; doi: 10.1002/acr.24564. Epub 2021 Jan 18. PMID: 33463020; PMCID: PMC8286274.
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Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures.

Frontiers in genetics

Yashin AI, Zhbannikov I, Arbeeva L, Arbeev KG, Wu D, Akushevich I, Yashkin A, Kovtun M, Kulminski AM, Stallard E, Kulminskaya I, Ukraintseva S.
PMID: 27877192
Front Genet. 2016 Nov 08;7:188. doi: 10.3389/fgene.2016.00188. eCollection 2016.

This paper shows that the effects of causal SNPs on lifespan, estimated through GWAS, may be confounded and the genetic structure of the study population may be responsible for this effect. Simulation experiments show that levels of linkage disequilibrium...

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Yashin AI, Zhbannikov I, Arbeeva L, et al. Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures. Front Genet. 2016;7:188doi: 10.3389/fgene.2016.00188.
Yashin, A. I., Zhbannikov, I., Arbeeva, L., Arbeev, K. G., Wu, D., Akushevich, I., Yashkin, A., Kovtun, M., Kulminski, A. M., Stallard, E., Kulminskaya, I., & Ukraintseva, S. (2016). Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures. Frontiers in genetics, 7188. https://doi.org/10.3389/fgene.2016.00188
Yashin, Anatoliy I, et al. "Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures." Frontiers in genetics vol. 7 (2016): 188. doi: https://doi.org/10.3389/fgene.2016.00188
Yashin AI, Zhbannikov I, Arbeeva L, Arbeev KG, Wu D, Akushevich I, Yashkin A, Kovtun M, Kulminski AM, Stallard E, Kulminskaya I, Ukraintseva S. Pure and Confounded Effects of Causal SNPs on Longevity: Insights for Proper Interpretation of Research Findings in GWAS of Populations with Different Genetic Structures. Front Genet. 2016 Nov 08;7:188. doi: 10.3389/fgene.2016.00188. eCollection 2016. PMID: 27877192; PMCID: PMC5099244.
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How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database.

Frontiers in public health

Arbeeva LS, Hanson HA, Arbeev KG, Kulminski AM, Stallard E, Ukraintseva SV, Wu D, Boudreau RM, Province MA, Smith KR, Yashin AI.
PMID: 30327761
Front Public Health. 2018 Oct 01;6:277. doi: 10.3389/fpubh.2018.00277. eCollection 2018.

The Family Longevity Selection Score (FLoSS) was used to select families for the Long Life Family Study (LLFS) but has never been validated in other populations. The goal of this paper is to validate how well the FLoSS-based selection...

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Arbeeva LS, Hanson HA, Arbeev KG, et al. How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database. Front Public Health. 2018;6:277doi: 10.3389/fpubh.2018.00277.
Arbeeva, L. S., Hanson, H. A., Arbeev, K. G., Kulminski, A. M., Stallard, E., Ukraintseva, S. V., Wu, D., Boudreau, R. M., Province, M. A., Smith, K. R., & Yashin, A. I. (2018). How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database. Frontiers in public health, 6277. https://doi.org/10.3389/fpubh.2018.00277
Arbeeva, Liubov S, et al. "How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database." Frontiers in public health vol. 6 (2018): 277. doi: https://doi.org/10.3389/fpubh.2018.00277
Arbeeva LS, Hanson HA, Arbeev KG, Kulminski AM, Stallard E, Ukraintseva SV, Wu D, Boudreau RM, Province MA, Smith KR, Yashin AI. How Well Does the Family Longevity Selection Score Work: A Validation Test Using the Utah Population Database. Front Public Health. 2018 Oct 01;6:277. doi: 10.3389/fpubh.2018.00277. eCollection 2018. PMID: 30327761; PMCID: PMC6174319.
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[No title available]

F1000Research

Zhbannikov IY, Arbeev K, Ukraintseva S, Yashin AI.
PMID: 28620455
F1000Res. 2017 Feb 01;6:97. doi: 10.12688/f1000research.10742.2. eCollection 2017.

We developed

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Zhbannikov IY, Arbeev K, Ukraintseva S, et al. . F1000Res. 2017;6:97doi: 10.12688/f1000research.10742.2.
Zhbannikov, I. Y., Arbeev, K., Ukraintseva, S., & Yashin, A. I. (2017). . F1000Research, 697. https://doi.org/10.12688/f1000research.10742.2
Zhbannikov, Ilya Y, et al. "." F1000Research vol. 6 (2017): 97. doi: https://doi.org/10.12688/f1000research.10742.2
Zhbannikov IY, Arbeev K, Ukraintseva S, Yashin AI. . F1000Res. 2017 Feb 01;6:97. doi: 10.12688/f1000research.10742.2. eCollection 2017. PMID: 28620455; PMCID: PMC5461892.
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Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk.

Frontiers in public health

Arbeev KG, Cohen AA, Arbeeva LS, Milot E, Stallard E, Kulminski AM, Akushevich I, Ukraintseva SV, Christensen K, Yashin AI.
PMID: 26835445
Front Public Health. 2016 Jan 25;4:3. doi: 10.3389/fpubh.2016.00003. eCollection 2016.

While longitudinal changes in biomarker levels and their impact on health have been characterized for individual markers, little is known about how overall marker profiles may change during aging and affect mortality risk. We implemented the recently developed measure...

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Arbeev KG, Cohen AA, Arbeeva LS, et al. Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk. Front Public Health. 2016;4:3doi: 10.3389/fpubh.2016.00003.
Arbeev, K. G., Cohen, A. A., Arbeeva, L. S., Milot, E., Stallard, E., Kulminski, A. M., Akushevich, I., Ukraintseva, S. V., Christensen, K., & Yashin, A. I. (2016). Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk. Frontiers in public health, 43. https://doi.org/10.3389/fpubh.2016.00003
Arbeev, Konstantin G, et al. "Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk." Frontiers in public health vol. 4 (2016): 3. doi: https://doi.org/10.3389/fpubh.2016.00003
Arbeev KG, Cohen AA, Arbeeva LS, Milot E, Stallard E, Kulminski AM, Akushevich I, Ukraintseva SV, Christensen K, Yashin AI. Optimal Versus Realized Trajectories of Physiological Dysregulation in Aging and Their Relation to Sex-Specific Mortality Risk. Front Public Health. 2016 Jan 25;4:3. doi: 10.3389/fpubh.2016.00003. eCollection 2016. PMID: 26835445; PMCID: PMC4725219.
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Cancer Risk and Behavioral Factors, Comorbidities, and Functional Status in the US Elderly Population.

ISRN oncology

Akushevich I, Kravchenko J, Akushevich L, Ukraintseva S, Arbeev K, Yashin A.
PMID: 22084731
ISRN Oncol. 2011;2011:415790. doi: 10.5402/2011/415790. Epub 2011 Jul 12.

About 80% of all cancers are diagnosed in the elderly and up to 75% of cancers are associated with behavioral factors. An approach to estimate the contribution of various measurable factors, including behavior/lifestyle, to cancer risk in the US...

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Akushevich I, Kravchenko J, Akushevich L, et al. Cancer Risk and Behavioral Factors, Comorbidities, and Functional Status in the US Elderly Population. ISRN Oncol. 2011;2011:415790doi: 10.5402/2011/415790.
Akushevich, I., Kravchenko, J., Akushevich, L., Ukraintseva, S., Arbeev, K., & Yashin, A. (2011). Cancer Risk and Behavioral Factors, Comorbidities, and Functional Status in the US Elderly Population. ISRN oncology, 2011415790. https://doi.org/10.5402/2011/415790
Akushevich, Igor, et al. "Cancer Risk and Behavioral Factors, Comorbidities, and Functional Status in the US Elderly Population." ISRN oncology vol. 2011 (2011): 415790. doi: https://doi.org/10.5402/2011/415790
Akushevich I, Kravchenko J, Akushevich L, Ukraintseva S, Arbeev K, Yashin A. Cancer Risk and Behavioral Factors, Comorbidities, and Functional Status in the US Elderly Population. ISRN Oncol. 2011;2011:415790. doi: 10.5402/2011/415790. Epub 2011 Jul 12. PMID: 22084731; PMCID: PMC3197174.
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The Prevalence of Knee Symptoms, Radiographic, and Symptomatic Osteoarthritis at Four Time Points: The Johnston County Osteoarthritis Project, 1999-2018.

ACR open rheumatology

Nelson AE, Hu D, Arbeeva L, Alvarez C, Cleveland RJ, Schwartz TA, Murphy LB, Helmick CG, Callahan LF, Renner JB, Jordan JM, Golightly YM.
PMID: 34245232
ACR Open Rheumatol. 2021 Aug;3(8):558-565. doi: 10.1002/acr2.11295. Epub 2021 Jul 10.

OBJECTIVE: To describe point prevalence of knee symptoms, radiographic knee osteoarthritis (rKOA), severe rKOA, and symptomatic rKOA at four time points in the longitudinal, population-based Johnston County Osteoarthritis Project (JoCo OA).METHODS: Data were from 2573 JoCo OA participants with...

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Nelson AE, Hu D, Arbeeva L, et al. The Prevalence of Knee Symptoms, Radiographic, and Symptomatic Osteoarthritis at Four Time Points: The Johnston County Osteoarthritis Project, 1999-2018. ACR Open Rheumatol. 2021;3(8):558-565doi: 10.1002/acr2.11295.
Nelson, A. E., Hu, D., Arbeeva, L., Alvarez, C., Cleveland, R. J., Schwartz, T. A., Murphy, L. B., Helmick, C. G., Callahan, L. F., Renner, J. B., Jordan, J. M., & Golightly, Y. M. (2021). The Prevalence of Knee Symptoms, Radiographic, and Symptomatic Osteoarthritis at Four Time Points: The Johnston County Osteoarthritis Project, 1999-2018. ACR open rheumatology, 3(8), 558-565. https://doi.org/10.1002/acr2.11295
Nelson, Amanda E, et al. "The Prevalence of Knee Symptoms, Radiographic, and Symptomatic Osteoarthritis at Four Time Points: The Johnston County Osteoarthritis Project, 1999-2018." ACR open rheumatology vol. 3,8 (2021): 558-565. doi: https://doi.org/10.1002/acr2.11295
Nelson AE, Hu D, Arbeeva L, Alvarez C, Cleveland RJ, Schwartz TA, Murphy LB, Helmick CG, Callahan LF, Renner JB, Jordan JM, Golightly YM. The Prevalence of Knee Symptoms, Radiographic, and Symptomatic Osteoarthritis at Four Time Points: The Johnston County Osteoarthritis Project, 1999-2018. ACR Open Rheumatol. 2021 Aug;3(8):558-565. doi: 10.1002/acr2.11295. Epub 2021 Jul 10. PMID: 34245232; PMCID: PMC8363850.
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Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort.

ACR open rheumatology

Golightly YM, Alvarez C, Arbeeva LS, Cleveland RJ, Schwartz TA, Renner JB, Murphy LB, Callahan LF, Jordan JM, Nelson AE.
PMID: 34196495
ACR Open Rheumatol. 2021 Aug;3(8):512-521. doi: 10.1002/acr2.11287. Epub 2021 Jul 01.

OBJECTIVE: To examine relationships between knee osteoarthritis (KOA) and obesity, diabetes mellitus (DM), and cardiovascular disease (CVD).METHODS: Associations of time-dependent obesity, DM, and CVD with KOA transition states over approximately 18 years were examined among 4093 participants from a...

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Golightly YM, Alvarez C, Arbeeva LS, et al. Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort. ACR Open Rheumatol. 2021;3(8):512-521doi: 10.1002/acr2.11287.
Golightly, Y. M., Alvarez, C., Arbeeva, L. S., Cleveland, R. J., Schwartz, T. A., Renner, J. B., Murphy, L. B., Callahan, L. F., Jordan, J. M., & Nelson, A. E. (2021). Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort. ACR open rheumatology, 3(8), 512-521. https://doi.org/10.1002/acr2.11287
Golightly, Yvonne M, et al. "Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort." ACR open rheumatology vol. 3,8 (2021): 512-521. doi: https://doi.org/10.1002/acr2.11287
Golightly YM, Alvarez C, Arbeeva LS, Cleveland RJ, Schwartz TA, Renner JB, Murphy LB, Callahan LF, Jordan JM, Nelson AE. Associations of Comorbid Conditions and Transitions Across States of Knee Osteoarthritis in a Community-Based Cohort. ACR Open Rheumatol. 2021 Aug;3(8):512-521. doi: 10.1002/acr2.11287. Epub 2021 Jul 01. PMID: 34196495; PMCID: PMC8363852.
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Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis.

Osteoarthritis and cartilage

Rushing BR, McRitchie S, Arbeeva L, Nelson AE, Azcarate-Peril MA, Li YY, Qian Y, Pathmasiri W, Sumner SCJ, Loeser RF.
PMID: 34718137
Osteoarthritis Cartilage. 2021 Oct 28; doi: 10.1016/j.joca.2021.10.006. Epub 2021 Oct 28.

OBJECTIVE: The objective of this exploratory study was to determine if perturbations in gut microbial composition and the gut metabolome could be linked to individuals with obesity and osteoarthritis (OA).METHODS: Fecal samples were collected from obese individuals diagnosed with...

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Rushing BR, McRitchie S, Arbeeva L, et al. Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis. Osteoarthritis Cartilage. 2021;doi: 10.1016/j.joca.2021.10.006.
Rushing, B. R., McRitchie, S., Arbeeva, L., Nelson, A. E., Azcarate-Peril, M. A., Li, Y. Y., Qian, Y., Pathmasiri, W., Sumner, S. C. J., & Loeser, R. F. (2021). Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis. Osteoarthritis and cartilage, . https://doi.org/10.1016/j.joca.2021.10.006
Rushing, B R, et al. "Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis." Osteoarthritis and cartilage vol. (2021). doi: https://doi.org/10.1016/j.joca.2021.10.006
Rushing BR, McRitchie S, Arbeeva L, Nelson AE, Azcarate-Peril MA, Li YY, Qian Y, Pathmasiri W, Sumner SCJ, Loeser RF. Fecal metabolomics reveals products of dysregulated proteolysis and altered microbial metabolism in obesity-related osteoarthritis. Osteoarthritis Cartilage. 2021 Oct 28; doi: 10.1016/j.joca.2021.10.006. Epub 2021 Oct 28. PMID: 34718137.
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