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      Type 2 diabetes mellitus does not increase the risk of multiple myeloma: a systematic review and meta-analysis

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          Abstract

          Background

          Epidemiological studies have shown that patients with type 2 diabetes mellitus (T2DM) are at a higher risk of secondary tumors. However, no consensus has been made about whether T2DM can increase the risk of multiple myeloma (MM).

          Methods

          We searched the databases of PubMed, Cochrane Library and EMBASE and cross-checked the bibliography. Data quality was assessed using the Newcastle-Ottawa scale (NOS). Heterogeneity was calculated as the odds ratio (OR) using a random-effects model. Data were analyzed using Stata version 12.0 software.

          Results

          A total of 13 articles were selected into this meta-analysis. Initially, we found that diabetic patients had a higher risk of myeloma than non-diabetic patients (OR =1.60, 95% CI: 1.13–2.26, I 2=98%, P=0.000). But the data in these articles were highly heterogeneous (I 2>75%). Therefore, eight of the included articles showed a moderate heterogeneity (I 2=71.6%). We used Galbraith heterogeneity map to analyze the causes of heterogeneity. Two articles with high heterogeneity were excluded. Then, we found the heterogeneity of the left six articles was reduced from moderate to mild (I 2=45.9%, P=0.100). The final results of this meta-analysis showed that T2DM was not a risk factor for increased incidence of MM (OR =1.05, 95% CI: 0.83–1.33, I 2=45.9%, P=0.100). Also, the subgroup analysis (case-control studies vs. cohort studies) showed no statistical difference (OR =1.19, 95% CI: 0.76–1.85, I 2=1%, P=0.364; OR =1.00, 95% CI: 0.75–1.33, I 2=71.2%, P=0.031; respectively).

          Conclusions

          T2DM is not a risk factor for the increased incidence of MM, a finding that should be validated with more strictly designed randomized controlled trials (RCTs).

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          Most cited references42

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          Measuring inconsistency in meta-analyses.

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            Bias in meta-analysis detected by a simple, graphical test

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              Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

              Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017) includes a comprehensive assessment of incidence, prevalence, and years lived with disability (YLDs) for 354 causes in 195 countries and territories from 1990 to 2017. Previous GBD studies have shown how the decline of mortality rates from 1990 to 2016 has led to an increase in life expectancy, an ageing global population, and an expansion of the non-fatal burden of disease and injury. These studies have also shown how a substantial portion of the world's population experiences non-fatal health loss with considerable heterogeneity among different causes, locations, ages, and sexes. Ongoing objectives of the GBD study include increasing the level of estimation detail, improving analytical strategies, and increasing the amount of high-quality data. Methods We estimated incidence and prevalence for 354 diseases and injuries and 3484 sequelae. We used an updated and extensive body of literature studies, survey data, surveillance data, inpatient admission records, outpatient visit records, and health insurance claims, and additionally used results from cause of death models to inform estimates using a total of 68 781 data sources. Newly available clinical data from India, Iran, Japan, Jordan, Nepal, China, Brazil, Norway, and Italy were incorporated, as well as updated claims data from the USA and new claims data from Taiwan (province of China) and Singapore. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between rates of incidence, prevalence, remission, and cause of death for each condition. YLDs were estimated as the product of a prevalence estimate and a disability weight for health states of each mutually exclusive sequela, adjusted for comorbidity. We updated the Socio-demographic Index (SDI), a summary development indicator of income per capita, years of schooling, and total fertility rate. Additionally, we calculated differences between male and female YLDs to identify divergent trends across sexes. GBD 2017 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting. Findings Globally, for females, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and haemoglobinopathies and haemolytic anaemias in both 1990 and 2017. For males, the causes with the greatest age-standardised prevalence were oral disorders, headache disorders, and tuberculosis including latent tuberculosis infection in both 1990 and 2017. In terms of YLDs, low back pain, headache disorders, and dietary iron deficiency were the leading Level 3 causes of YLD counts in 1990, whereas low back pain, headache disorders, and depressive disorders were the leading causes in 2017 for both sexes combined. All-cause age-standardised YLD rates decreased by 3·9% (95% uncertainty interval [UI] 3·1–4·6) from 1990 to 2017; however, the all-age YLD rate increased by 7·2% (6·0–8·4) while the total sum of global YLDs increased from 562 million (421–723) to 853 million (642–1100). The increases for males and females were similar, with increases in all-age YLD rates of 7·9% (6·6–9·2) for males and 6·5% (5·4–7·7) for females. We found significant differences between males and females in terms of age-standardised prevalence estimates for multiple causes. The causes with the greatest relative differences between sexes in 2017 included substance use disorders (3018 cases [95% UI 2782–3252] per 100 000 in males vs s1400 [1279–1524] per 100 000 in females), transport injuries (3322 [3082–3583] vs 2336 [2154–2535]), and self-harm and interpersonal violence (3265 [2943–3630] vs 5643 [5057–6302]). Interpretation Global all-cause age-standardised YLD rates have improved only slightly over a period spanning nearly three decades. However, the magnitude of the non-fatal disease burden has expanded globally, with increasing numbers of people who have a wide spectrum of conditions. A subset of conditions has remained globally pervasive since 1990, whereas other conditions have displayed more dynamic trends, with different ages, sexes, and geographies across the globe experiencing varying burdens and trends of health loss. This study emphasises how global improvements in premature mortality for select conditions have led to older populations with complex and potentially expensive diseases, yet also highlights global achievements in certain domains of disease and injury. Funding Bill & Melinda Gates Foundation.
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                Author and article information

                Journal
                Transl Cancer Res
                Transl Cancer Res
                TCR
                Translational Cancer Research
                AME Publishing Company
                2218-676X
                2219-6803
                April 2020
                April 2020
                : 9
                : 4
                : 2884-2894
                Affiliations
                [1 ]Department of Hematology, The Affiliated Hospital of Nantong University, Nantong 226001, China, ;
                [2 ]deptDepartment of Tongji Medical College , Huazhong University of Science and Technology , Wuhan 430074, China;
                [3 ] Department of Medical Informatics, Medical School of Nantong University, Nantong 226001, China,
                Author notes

                Contributions: (I) Conception and design: H Huang, L Shi; (II) Administrative support: H Huang, L Shi; (III) Provision of study materials: D Guo, Y Jiang, L Hong; (IV) Collection and assembly of data: C Zhang, H Liu; (V) Data analysis and interpretation: C Zhang, Y Sha; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

                [#]

                These authors contributed equally to this work.

                Correspondence to: Hongming Huang. Department of Hematology, The Affiliated Hospital of Nantong University, Nantong 226001, China. Email: hhmmmc@ 123456163.com ; Lili Shi. Department of Medical Informatics, Medical School of Nantong University, Nantong 226001, China. Email: shilili@ 123456ntu.edu.cn .
                Article
                tcr-09-04-2884
                10.21037/tcr.2020.03.36
                8798954
                35117645
                5ac5bf86-a7d3-4560-bfa5-4e426810ed1e
                2020 Translational Cancer Research. All rights reserved.

                Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 13 December 2019
                : 28 February 2020
                Funding
                Funded by: Nantong Science and Technology Project
                Award ID: grant No. JCZ19029
                Award ID: No. MS22019003
                Categories
                Original Article

                meta-analysis,type 2 diabetes mellitus (t2dm),multiple myeloma (mm),carcinogenic risk

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