Gut microbiota in subclinical carotid atherosclerosis, Milan
Cross-sectional study of 46 fecal metagenome samples from adults in Milan, Italy, comparing gut microbiota composition between subjects with subclinical carotid atherosclerosis (23) and healthy individuals, linked to dietary intake. machine-written summary
46 samples · 46 runs · IT · Adult · 2021 · 4.28 Gbp / sample
Checked this project's papers and archive records yourself? Mark it human-verified.
what each source gave
| ✓ Sequence archive | 230 sample values (country, health condition, health condition detail, location locality, subject id) |
| ○ Abstract / description | read; no usable statement |
| ○ Full text | read; no usable statement or table · PMC7911134 |
| ○ Supplementary tables | files read; no table could be matched to the samples |
| – External resources | not covered by the external resources we ingest |
| – User contribution | nothing uploaded yet |
| – Human verified | not yet checked by a person · I checked this project — verify it |
Genera, mean relative abundance over 40 profiled gut samples of this project (Sandpiper SingleM profiles, GTDB R232; low-depth and non-metagenome profiles left out).
| field | samples | coverage | R1 | R2 | R3 | R4 |
|---|---|---|---|---|---|---|
| age_category | 46 | 100% | ||||
| country | 46 | 100% | 46 | |||
| health_condition | 46 | 100% | 46 | |||
| subject_id | 46 | 100% | 46 | |||
| detailed_location | 46 | 100% | ||||
| health_condition_detail | 46 | 100% | 46 | |||
| location_locality | 46 | 100% | 46 |
- Gut Microbiota Functional Dysbiosis Relates to Individual Diet in Subclinical Carotid Atherosclerosis. 2021 PMID 33494335 PMC7911134 own_data
- Annotation-free sequence-level multimodal graph learning reveals gut microbiome signatures of atherosclerotic cardiovascular disease 2026 own_data
- Multi-omics integration reveals functional signatures of gut microbiome in atherosclerosis. 2025 PMID 40785047 PMC12341049 unsure
Archive description
Gut Microbiota (GM) dysbiosis associates with Atherosclerotic Cardiovascular Diseases (ACVD), but whether this also holds true in subjects without clinically manifest ACVD represents a challenge of personalized prevention. We connected exposure to diet (self-reported by food diaries) and markers of Subclinical Carotid Atherosclerosis (SCA) with individual taxonomic and functional GM profiles (from fecal metagenomic DNA) of 345 subjects without previous clinically manifest ACVD. We took advantage of 16s rRNA-based analysis to characterize taxonomic GM composition on an epidemiological study representative of the general population characterized by low CVD risk and analyzed for presence of subclinical carotid atherosclerosis (SCA) then, advanced metagenomic analysis on a subset of 23 samples characterized by presence of SCA and intima-media thickning and 23 healthy individuals was set to derive functional relevance of dysbiosis in pre-clinical setting and to identify the bacterial species mainly responsible of GM dysregulation during SCA progression.
How this project entered the registry
Classified as human gut / stool shotgun DNA metagenome of adult by deterministic_prior with confidence 0.90. Assay shotgun_dna.
- host external_curation.infant_catalog · “infant triage reason_code age_adult_only”
- body site study.title · “Alteration in gut microbiota composition and”
- body site study.description · “Gut Microbiota (GM) dysbiosis”
- life stage external_curation.infant_catalog · “infant triage reason_code age_adult_only”
Evidence examples (one per field × route)
| sample | field | value | route | conf. | source | quote |
|---|---|---|---|---|---|---|
| SAMN14514435 | country | IT | R1 | 0.90 | sample.attr.geo_loc_name | Italy: Milano |
| SAMN14514435 | health_condition | healthy_control | R1 | 0.85 | biosample.attribute:SCA status | No SCA |
| SAMN14514451 | health_condition | other_disease | R1 | 0.70 | biosample_attr | SCA |
| SAMN14514435 | health_condition_detail | No SCA | R1 | 0.85 | biosample.attribute:SCA status | No SCA |
| SAMN14514435 | location_locality | Milan | R1 | 0.85 | biosample.attribute:geo_loc_name | Italy: Milano |
| SAMN14514435 | subject_id | 18 | R1 | 0.85 | sample.attr.host_subject_id | 18 |
Sequencing
46 runs over 46 samples · mean 4.45 Gbp per run (median 4.28 Gbp, total 204.69 Gbp; 46 runs with a base count) · layout PAIRED 46 · platform ILLUMINA 46 · models Illumina NovaSeq 6000 (46) · 87% of runs with a Sandpiper profile · first public 2021-01-19
Authors (49)
Baragetti A first · Severgnini M · Olmastroni E · Dioguardi CC · Mattavelli E · Angius A · Rotta L · Cibella J · Caredda G · Consolandi C · Grigore L · Pellegatta F · Giavarini F · Caruso D · Norata GD · Catapano AL · Peano C last · Argentini C first · Lugli GA · Tarracchini C · Fontana F · Mancabelli L · Viappiani A · Anzalone R · Angelini L · Alessandri G · Longhi G · Bianchi MG · Taurino G · Bussolati O · Milani C · van Sinderen D · Turroni F · Ventura M last · Shi H first · Wu M · Wu X · Liu Z · Jiang S · Li G · Yang Y · Fu Y · Wang Q · Zhang G · Cheng L last · Hu W first · Wang W · Zhang L · Fu YV last
Downloads
PRJNA615842.csv.gz (2.2 KB) · parquet (70.9 KB) — all 46 samples, every field with route and confidence · PRJNA615842_determinations.csv.gz (2.3 KB) — 230 evidence rows