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Record W4303575232 · doi:10.1016/j.heliyon.2022.e11001

Influence of type 2 diabetes microangiopathy on bone mineral density and bone metabolism: A meta-analysis

2022· article· en· W4303575232 on OpenAlexaboutno aff
Jinlong Zhao, Guihong Liang, Miaohui Luo, Weiyi Yang, Nanjun Xu, Minghui Luo, Jianke Pan, Jun Liu, Lingfeng Zeng

Bibliographic record

VenueHeliyon · 2022
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaScience and Technology Planning Project of Guangdong ProvinceGuangdong Medical Research FoundationNational Natural Science Foundation of China
KeywordsMicroangiopathyMedicineBone mineralInternal medicineType 2 diabetesDiabetes mellitusCochrane LibraryMeta-analysisBone remodelingFemoral neckOsteoporosisEndocrinology

Abstract

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Background Diabetic microangiopathy is a type of vascular dysfunction. The effect of type 2 diabetes microangiopathy (DMA) on bone mineral density (BMD) and bone metabolism is still unclear. Objective A meta-analysis was performed to investigate the effects of microangiopathy on BMD and bone metabolism in type 2 diabetic patients. Methods We searched the PubMed, Embase, Cochrane Library and CNKI databases to identify observational studies investigating the effects of type 2 diabetes microangiopathy on BMD or bone metabolism. The time limit for the literature retrieval was from the establishment of the database to September 25, 2021. The Newcastle–Ottawa scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) scale were used to evaluate the quality of the studies. RevMan 5.3 software was used for the data analysis. Stata 14.0 was used to quantitatively evaluate the publication bias of the outcome indicators. Results In total, 12 observational studies were included, including 7 cohort studies, 4 case–control studies and 1 cross-sectional study. In total, 2,500 patients with type 2 diabetes were included. Among them, 1,249 patients had microangiopathy (DMA group), and 1,251 patients did not have microangiopathy (control group). The results of the meta-analysis showed that the BMDs of the femoral neck (SMD = −1.34, 95% CI=−2.22 to −0.45, P=0.003), lumbar spine (SMD = −0.69, 95% CI=−1.31 to −0.08, P=0.03) and Ward's triangle (SMD = −2.84, 95% CI=−4.84 to −0.83, P=0.006) in the DMA group were lower than those in the control group. In the comparison of the bone metabolism indexes, the contents of N-terminal propeptide of type I procollagen (P1NP) (SMD = 0.18, 95% CI=0.03 to 0.32, P=0.02), osteocalcin (SMD = 6.97, 95% CI=3.46 to 10.48, P < 0. 0001), parathyroid hormone (PTH) (SMD = 0.38, 95% CI=0.03 to 0.73, P=0.03) and C-telopeptide of type 1 collagen (CTX) (SMD = 0.39, 95% CI=0.03 to 0.75, P=0.03) in serum from the DMA group were higher than those in serum from the control group. The serum content of 25-hydroxyvitamin D 3 (25(OH)D 3 ) (SMD = −0.63, 95% CI=−1.19 to −0.07, P=0.03) in the DMA group was lower than that in the control group. There was no significant difference in serum alkaline phosphatase (ALP), calcium or phosphorus between the two groups (P > 0.05). Conclusions Type 2 diabetes microangiopathy can reduce the lumbar spine, femoral neck and Ward's triangle BMD and has a higher risk of osteoporosis or osteoporosis fractures. The levels of P1NP, PTH, CTX and OC in the serum of patients with type 2 diabetes microangiopathy are higher, and the lower 25(OH)D 3 content may be a mechanism by which DMA destroys bone metabolism balance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.266
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2022
Admission routes1
Has abstractyes

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