Diabetic Foot OsteomyelitisNCT04240964
Studying on the Difference Between Two Kinds of Osteomyelitis
An observational study of Diabetic Foot Osteomyelitis and Diabetic Foot Infection, run by Nanfang Hospital, Southern Medical University at 1 site in China.
Trial summary
To explore the microbial differences of diabetic foot osteomyelitis and osteomyelitis without diabetes. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Diabetic Foot Osteomyelitis, Diabetic Foot Infection, Microbiome
- How many people
- 28 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2017to 1 Dec 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients with osteomyelitis
Who may be able to join
- age≥18 years old.
- diagnosed as foot osteomyelitis, and wounds were located below the knee joint.
- nfected bone exposure or positive probe-to-bone test.
- The patients were good physical condition.
- Patients were able to tolerate debridement or operation treatment.
- patients and their families agreed to participate in the study.
Who cannot join
- Serious skin diseases around the wound surface;
- tumors affecting the wound of osteomyelitis;
- long-term use of immunosuppressive therapy before admission;
- patients that do not cooperate.
Trial locations and contacts
1 site in 1 country.
China1 site
Department of Endocrinology and Metabolism, NanFang Hospital, Southern Medical University
Guangzhou, Guangdong, 510515
About this study
Official title: Studying on the Difference Between Foot Osteomyelitis Patients With or Without Diabetes
To explore the microbial differences of diabetic foot osteomyelitis and osteomyelitis without diabetes.
Read the detailed description
Diabetic foot osteomyelitis (Dd group) subjects (sample size ≥ 10 cases) and foot osteomyelitis without diabetes (ND group) subjects (sample size ≥ 10 cases) were collected in accordance with the inclusion criteria. Surgeons collected intra-operative bone specimens from all 28 patients who required surgical intervention (debridement or amputation) for management of their osteomyelitis[10]. Avoided soft tissue or sinus tract cultures, because they were not sufficiently accurate in predicting bone pathogens[11]. Following surgical debridement of infected or necrotic tissue, cleansed by sterile saline solution, deep bone specimens were harvested using sterile instruments. We routinely obtained adequate bone specimens and divided them into three parts. One for routine microbiology culture and antibiotic sensitivity testing, one for the histopathology, and the other one for DNA sequencing analysis. This process to obtain bone specimens could reduce chances of contamination by wound tissue colonizing bacteria.
16S rRNA high-throughput sequencing Two specimens were sent to the laboratory for conventional culturing and histopathological tests. The bone specimen left were placed in sterile pipes without any transport medium and stored at 4℃ for 24 hours and then frozen at -80C until DNA extraction. Genomic DNA was extracted using DNA extraction kit (YiRui,ShenZhen,China) according to the manufacturer's instructions. Extracted DNA was quantitative and quality control by agarose gel electrophoresis(JS-power 300, PeiQin, ShangHai, China). Then we amplified the V3-V4 variable region of the 16S rRNA gene for sequencing using a forward and a reverse fusion primer-(341F:5'-CCTAYGGGRBGCASCAG-3' and 806R:5'-GGACTACNNGGGTATCTAAT-3)(ABI GeneAmp 9700 PCR Instrument). PCR products were amplified by removing short sequences, singleton sequences and noisy reads. The PCR reaction was performed in a total volume of 60 μl, containing 6 μl of 10× Ex Tap PCR buffer, 6 μl of dNTP mixture, 0.6μl of bovine serum albumin (BSA), 0.3 μl Ex Tag, 1μl DNA, 1.2μl forward and reverse primers, and 43.7 μlH 2O. The PCR amplification was conducted under following conditions: initial denaturing was conducted at 94°Cfor5min, which was followed by 27 cycles at94°Cfor30s, 55°Cfor30s, and72°C for 45s. A final extension was performed at 72°C for 10min from 28 samples (including DFO and SFO patients) were sequenced over two separate runs on Illumina Miseq. To get high-quality clean reads, raw reads were filtered according to the following rules: (1) remove reads containing morethan10% ofunknown nucleotides and(2) removereads containing less than 80% of bases with quality (Q-value)>20. The filtered reads were then assembled into tags according to overlap between paired-end reads with more than 10bp overlap, and less than 2% mismatch. The software Mothur (v.1.34.0) was used to remove the redundant tags to get unique tags. The obtained unique tags were then used to calculate the abundance.Then we clustered sequences into operational taxonomic units (OTUs) using the Greengene. Taxonomy was assigned to OTUs using the BLASTto the Greengene database at 97% similarity to identify microorganisms at the genera level (species level where possible). A phylogenetic tree was built from aligned representative OTU sequences using figtree.The total species diversity in a landscape was determined by two different parameters, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity). Alpha diversity included both community diversity and richness: community richness was represented by the ACE estimator or the Chao1 estimator. Beta diversity was the calculation of differences(distance) between microbiome community structure and membership based on the evolution of species. This kind of distance was calculated using Weighted Unifrac and can be performanced by Principal Co-ordinates Analysis diagram.
Metagenomes Analysis A metagenome DNA libraries from specimens were constructed using TruSeq Nano DNA kit (FC-121-4002) according to the manufacturer's instructions, with slight modifications. In brief, the length conformed DNA(350 bp) was obtained by sonication. DNA fragments were end-repaired and the appropriate library size was selected, then the samples were A-tailed and ligated to adapters. The NovaSeq 6000 sequencing systems(Illumina)were used for sequencing and library validation Raw Data obtained by sequencing have a certain proportion of low-quality dataaccording to the following rules: (1) remove reads containing morethan10% ofunknown nucleotides and(2) removereads containing less than 80% of bases with quality (Q-value)>20 . Megahit was used to splice the sequences(clean data) after quality control, and contigs were obtained.Contigs were filtered below 1000bp. The vector and host sequences were filtered by BLASTN, with an E-value cutoff of 1e-5. The remaining reads were mapped to the human genome by SOAP alignment, and the matching reads were removed as being contaminants from the host genome.The taxonomic classifications were performed on assembled contigs and singletons using BLAST against the NCBI database. And the best BLAST hit was used to refer the taxonomic rank of each sequence. All the analyses have been performed in R and p values were corrected for multiple testing with the false discovery rate method.
- Keywords
- Diabetic Foot Osteomyelitis, 16S rRNA, Metagenomes, Microbiome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Other
| Study group | Role | What they receive |
|---|---|---|
| Dd group | Patients with diabetic foot osteomyelitis | |
| ND group | Foot osteomyelitis without diabetes |
What is being measured
The primary outcome is the main question the study is designed to answer.
Composition and Diversity of DFO with 16S rRNA Gene Data
Subjects who met the inclusion criteria for diabetic foot osteomyelitis (DFO) (sample size ≥ 10) and osteomyelitis without NDFO (sample size ≥ 10) were collected for debridement and amputation for 16s rDNA high-throughput sequencing. Compare microbe composition and diversity between the two groups.
Measured: 2017-2018
Composition of the DFO Metagenome
Subjects who met the inclusion criteria for diabetic foot osteomyelitis (DFO) (sample size ≥ 10) and osteomyelitis without NDFO (sample size ≥ 10) were collected for debridement and amputation for Metagenomic Analysis. Compare microbe composition and diversity between the two groups.
Measured: 2018-2019
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jan 2020
- Study start1 Jul 2017
- Primary completion1 Dec 2018
- Study completion1 Dec 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (7)Papers the sponsor has linked to this study.
- Other trials for Diabetic Foot OsteomyelitisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 28 participants (actual)
Sponsor and investigators
- Lead sponsor
- Nanfang Hospital, Southern Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ying Cao, Nanfang Hospital, Southern Medical University
- Investigators
- ying cao, doctor, Principal investigator, Nanfang Hospital, Southern Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04240964
- Sponsor's protocol ID
- NFEC-2017-013
- Status last verified
- Jan 2020
- First submitted
- 28 Oct 2019
- Last updated
- 27 Jan 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Lavery LA, Peters EJ, Armstrong DG, Wendel CS, Murdoch DP, Lipsky BA. Risk factors for developing osteomyelitis in patients with diabetic foot wounds. Diabetes Res Clin Pract. 2009 Mar;83(3):347-52. doi: 10.1016/j.diabres.2008.11.030. Epub 2008 Dec 30.
PubMed 19117631Berendt AR, Peters EJ, Bakker K, Embil JM, Eneroth M, Hinchliffe RJ, Jeffcoate WJ, Lipsky BA, Senneville E, Teh J, Valk GD. Diabetic foot osteomyelitis: a progress report on diagnosis and a systematic review of treatment. Diabetes Metab Res Rev. 2008 May-Jun;24 Suppl 1:S145-61. doi: 10.1002/dmrr.836.
PubMed 18442163Valabhji J, Oliver N, Samarasinghe D, Mali T, Gibbs RG, Gedroyc WM. Conservative management of diabetic forefoot ulceration complicated by underlying osteomyelitis: the benefits of magnetic resonance imaging. Diabet Med. 2009 Nov;26(11):1127-34. doi: 10.1111/j.1464-5491.2009.02828.x.
PubMed 19929991Huang Y, Cao Y, Zou M, Luo X, Jiang Y, Xue Y, Gao F. A Comparison of Tissue versus Swab Culturing of Infected Diabetic Foot Wounds. Int J Endocrinol. 2016;2016:8198714. doi: 10.1155/2016/8198714. Epub 2016 Mar 30.
PubMed 27123004Goda A, Maruyama F, Michi Y, Nakagawa I, Harada K. Analysis of the factors affecting the formation of the microbiome associated with chronic osteomyelitis of the jaw. Clin Microbiol Infect. 2014 May;20(5):O309-17. doi: 10.1111/1469-0691.12400. Epub 2013 Nov 11.
PubMed 24112138Redel H, Gao Z, Li H, Alekseyenko AV, Zhou Y, Perez-Perez GI, Weinstock G, Sodergren E, Blaser MJ. Quantitation and composition of cutaneous microbiota in diabetic and nondiabetic men. J Infect Dis. 2013 Apr;207(7):1105-14. doi: 10.1093/infdis/jit005. Epub 2013 Jan 8.
PubMed 23300163Zou M, Cai Y, Hu P, Cao Y, Luo X, Fan X, Zhang B, Wu X, Jiang N, Lin Q, Zhou H, Xue Y, Gao F. Analysis of the Composition and Functions of the Microbiome in Diabetic Foot Osteomyelitis Based on 16S rRNA and Metagenome Sequencing Technology. Diabetes. 2020 Nov;69(11):2423-2439. doi: 10.2337/db20-0503. Epub 2020 Aug 14.
PubMed 32801139
Study at a glance
From the registry record, last updated by the sponsor on 27 Jan 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Studying on the Difference Between Foot Osteomyelitis Patients With or Without Diabetes
- ClinicalTrials.gov ID
- NCT04240964
- Acronym / sponsor ID
- NFEC-2017-013
- Keywords
- Diabetic Foot Osteomyelitis, 16S rRNA, Metagenomes, Microbiome
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 28 participants (actual)
- Lead sponsor
- Nanfang Hospital, Southern Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2017 (actual)
- Primary completion
- 1 Dec 2018 (actual)
- Study completion
- 1 Dec 2018 (actual)
- First posted
- 27 Jan 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
To explore the microbial differences of diabetic foot osteomyelitis and osteomyelitis without diabetes.
Detailed description
Diabetic foot osteomyelitis (Dd group) subjects (sample size ≥ 10 cases) and foot osteomyelitis without diabetes (ND group) subjects (sample size ≥ 10 cases) were collected in accordance with the inclusion criteria. Surgeons collected intra-operative bone specimens from all 28 patients who required surgical intervention (debridement or amputation) for management of their osteomyelitis[10]. Avoided soft tissue or sinus tract cultures, because they were not sufficiently accurate in predicting bone pathogens[11]. Following surgical debridement of infected or necrotic tissue, cleansed by sterile saline solution, deep bone specimens were harvested using sterile instruments. We routinely obtained adequate bone specimens and divided them into three parts. One for routine microbiology culture and antibiotic sensitivity testing, one for the histopathology, and the other one for DNA sequencing analysis. This process to obtain bone specimens could reduce chances of contamination by wound tissue colonizing bacteria.
16S rRNA high-throughput sequencing Two specimens were sent to the laboratory for conventional culturing and histopathological tests. The bone specimen left were placed in sterile pipes without any transport medium and stored at 4℃ for 24 hours and then frozen at -80C until DNA extraction. Genomic DNA was extracted using DNA extraction kit (YiRui,ShenZhen,China) according to the manufacturer's instructions. Extracted DNA was quantitative and quality control by agarose gel electrophoresis(JS-power 300, PeiQin, ShangHai, China). Then we amplified the V3-V4 variable region of the 16S rRNA gene for sequencing using a forward and a reverse fusion primer-(341F:5'-CCTAYGGGRBGCASCAG-3' and 806R:5'-GGACTACNNGGGTATCTAAT-3)(ABI GeneAmp 9700 PCR Instrument). PCR products were amplified by removing short sequences, singleton sequences and noisy reads. The PCR reaction was performed in a total volume of 60 μl, containing 6 μl of 10× Ex Tap PCR buffer, 6 μl of dNTP mixture, 0.6μl of bovine serum albumin (BSA), 0.3 μl Ex Tag, 1μl DNA, 1.2μl forward and reverse primers, and 43.7 μlH 2O. The PCR amplification was conducted under following conditions: initial denaturing was conducted at 94°Cfor5min, which was followed by 27 cycles at94°Cfor30s, 55°Cfor30s, and72°C for 45s. A final extension was performed at 72°C for 10min from 28 samples (including DFO and SFO patients) were sequenced over two separate runs on Illumina Miseq. To get high-quality clean reads, raw reads were filtered according to the following rules: (1) remove reads containing morethan10% ofunknown nucleotides and(2) removereads containing less than 80% of bases with quality (Q-value)>20. The filtered reads were then assembled into tags according to overlap between paired-end reads with more than 10bp overlap, and less than 2% mismatch. The software Mothur (v.1.34.0) was used to remove the redundant tags to get unique tags. The obtained unique tags were then used to calculate the abundance.Then we clustered sequences into operational taxonomic units (OTUs) using the Greengene. Taxonomy was assigned to OTUs using the BLASTto the Greengene database at 97% similarity to identify microorganisms at the genera level (species level where possible). A phylogenetic tree was built from aligned representative OTU sequences using figtree.The total species diversity in a landscape was determined by two different parameters, the mean species diversity in sites or habitats at a more local scale (alpha diversity) and the differentiation among those habitats (beta diversity). Alpha diversity included both community diversity and richness: community richness was represented by the ACE estimator or the Chao1 estimator. Beta diversity was the calculation of differences(distance) between microbiome community structure and membership based on the evolution of species. This kind of distance was calculated using Weighted Unifrac and can be performanced by Principal Co-ordinates Analysis diagram.
Metagenomes Analysis A metagenome DNA libraries from specimens were constructed using TruSeq Nano DNA kit (FC-121-4002) according to the manufacturer's instructions, with slight modifications. In brief, the length conformed DNA(350 bp) was obtained by sonication. DNA fragments were end-repaired and the appropriate library size was selected, then the samples were A-tailed and ligated to adapters. The NovaSeq 6000 sequencing systems(Illumina)were used for sequencing and library validation Raw Data obtained by sequencing have a certain proportion of low-quality dataaccording to the following rules: (1) remove reads containing morethan10% ofunknown nucleotides and(2) removereads containing less than 80% of bases with quality (Q-value)>20 . Megahit was used to splice the sequences(clean data) after quality control, and contigs were obtained.Contigs were filtered below 1000bp. The vector and host sequences were filtered by BLASTN, with an E-value cutoff of 1e-5. The remaining reads were mapped to the human genome by SOAP alignment, and the matching reads were removed as being contaminants from the host genome.The taxonomic classifications were performed on assembled contigs and singletons using BLAST against the NCBI database. And the best BLAST hit was used to refer the taxonomic rank of each sequence. All the analyses have been performed in R and p values were corrected for multiple testing with the false discovery rate method.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Dd group | – | –Patients with diabetic foot osteomyelitis |
| ND group | – | –Foot osteomyelitis without diabetes |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with osteomyelitis
Key inclusion criteria (6 listed)
- age≥18 years old.
- diagnosed as foot osteomyelitis, and wounds were located below the knee joint.
- nfected bone exposure or positive probe-to-bone test.
- The patients were good physical condition.
- Patients were able to tolerate debridement or operation treatment.
Key exclusion criteria (4 listed)
- Serious skin diseases around the wound surface;
- tumors affecting the wound of osteomyelitis;
- long-term use of immunosuppressive therapy before admission;
- patients that do not cooperate.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Other | Dd group, ND group |
Primary outcome
Composition and Diversity of DFO with 16S rRNA Gene Data
Time frame: 2017-2018
Subjects who met the inclusion criteria for diabetic foot osteomyelitis (DFO) (sample size ≥ 10) and osteomyelitis without NDFO (sample size ≥ 10) were collected for debridement and amputation for 16s rDNA high-throughput sequencing. Compare microbe composition and diversity between the two groups.
Composition of the DFO Metagenome
Time frame: 2018-2019
Subjects who met the inclusion criteria for diabetic foot osteomyelitis (DFO) (sample size ≥ 10) and osteomyelitis without NDFO (sample size ≥ 10) were collected for debridement and amputation for Metagenomic Analysis. Compare microbe composition and diversity between the two groups.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jan 2020.
- Background: PubMed 19117631Lavery LA, Peters EJ, Armstrong DG, Wendel CS, Murdoch DP, Lipsky BA. Risk factors for developing osteomyelitis in patients with diabetic foot wounds. Diabetes Res Clin Pract. 2009 Mar;83(3):347-52. doi: 10.1016/j.diabres.2008.11.030. Epub 2008 Dec 30.
- Background: PubMed 18442163Berendt AR, Peters EJ, Bakker K, Embil JM, Eneroth M, Hinchliffe RJ, Jeffcoate WJ, Lipsky BA, Senneville E, Teh J, Valk GD. Diabetic foot osteomyelitis: a progress report on diagnosis and a systematic review of treatment. Diabetes Metab Res Rev. 2008 May-Jun;24 Suppl 1:S145-61. doi: 10.1002/dmrr.836.
- Background: PubMed 19929991Valabhji J, Oliver N, Samarasinghe D, Mali T, Gibbs RG, Gedroyc WM. Conservative management of diabetic forefoot ulceration complicated by underlying osteomyelitis: the benefits of magnetic resonance imaging. Diabet Med. 2009 Nov;26(11):1127-34. doi: 10.1111/j.1464-5491.2009.02828.x.
- Background: PubMed 27123004Huang Y, Cao Y, Zou M, Luo X, Jiang Y, Xue Y, Gao F. A Comparison of Tissue versus Swab Culturing of Infected Diabetic Foot Wounds. Int J Endocrinol. 2016;2016:8198714. doi: 10.1155/2016/8198714. Epub 2016 Mar 30.
- Background: PubMed 24112138Goda A, Maruyama F, Michi Y, Nakagawa I, Harada K. Analysis of the factors affecting the formation of the microbiome associated with chronic osteomyelitis of the jaw. Clin Microbiol Infect. 2014 May;20(5):O309-17. doi: 10.1111/1469-0691.12400. Epub 2013 Nov 11.
- Background: PubMed 23300163Redel H, Gao Z, Li H, Alekseyenko AV, Zhou Y, Perez-Perez GI, Weinstock G, Sodergren E, Blaser MJ. Quantitation and composition of cutaneous microbiota in diabetic and nondiabetic men. J Infect Dis. 2013 Apr;207(7):1105-14. doi: 10.1093/infdis/jit005. Epub 2013 Jan 8.
- Derived from this study: PubMed 32801139Zou M, Cai Y, Hu P, Cao Y, Luo X, Fan X, Zhang B, Wu X, Jiang N, Lin Q, Zhou H, Xue Y, Gao F. Analysis of the Composition and Functions of the Microbiome in Diabetic Foot Osteomyelitis Based on 16S rRNA and Metagenome Sequencing Technology. Diabetes. 2020 Nov;69(11):2423-2439. doi: 10.2337/db20-0503. Epub 2020 Aug 14.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
ying cao, doctor principal investigator
Nanfang Hospital, Southern Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabetic Foot Osteomyelitis and Diabetic Foot Infection, run by Nanfang Hospital, Southern Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 27 Jan 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Composition and Diversity of DFO with 16S rRNA Gene Data (time frame: 2017-2018). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04240964, which also lists 7 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jan 2020; AzTrials last refreshed its copy on 31 Oct 2026.