| Literature DB >> 36101810 |
Behnam Ghorbani Nejad1, Tahereh Raeisi2, Parisa Janmohammadi3, Fatemeh Mehravar4, Mahtab Zarei5, Azadeh Dehghani6, Niki Bahrampour7, Mohammad Hosein Darijani1, Fatemeh Ahmadipour1, Mohammad Mohajeri1, Shahab Alizadeh3.
Abstract
Methods: Scopus and PubMed databases were systematically searched from their inception to November 2021 to obtain pertinent studies. Standardized mean differences (SMDs) with corresponding 95% confidence intervals (CIs) were calculated to evaluate the difference in Hg levels between people with and without T2DM. The association of the Hg exposure with T2DM was assessed using a random-effects model by pooling the odds ratios (ORs) and 95% CIs.Entities:
Mesh:
Substances:
Year: 2022 PMID: 36101810 PMCID: PMC9463027 DOI: 10.1155/2022/7640227
Source DB: PubMed Journal: Int J Clin Pract ISSN: 1368-5031 Impact factor: 3.149
Figure 1Flowchart of the study.
The characteristics of the included studies in meta-analysis.
| Study | Country | Year | Study design | Sex | Total sample size | N cases with T2DM (age) | Sample source | Mean ± SD Hg in controls | Mean ± SD Hg in cases (T2DM) | Hg assessment | Type of effect size | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Anica Simi´c | Norway | 2017 | Case-control | Both | 874 | 267 (65.4 ± 10.6) | Blood | 3.19 | 3.6 ± 4.8 | ICP-MS | Mean of Hg in T2DM vs. controls | Adjusted for BMI, waist-to-hip ratio, first-degree family history of diabetes, smoking habits, area, education and economic status, fat fish intake |
|
| ||||||||||||
| Yohei Hotta | Japan | 2019 | Case-control | Male | 50 | 27 (66.1 ± 13.2) | Hair | 2.5 ± 2.94 | 2.12 ± 1.49 | ICP-MS | Mean of Hg in T2DM vs. controls | — |
| Female | 46 | 15 (68.0 ± 8.5) | 1.37 ± 1.03 | 2.19 ± 1.83 | ||||||||
|
| ||||||||||||
| Hassan Imran Afridi | Pakistan | 2015 | Case-control | Male | 43 | 25 (ranged 30–50 years) | Hair | 1.02 ± 0.07 | 1.69 ± 0.08 | ICP-atomic emission spectrophotometer | Mean of Hg in T2DM vs. controls | — |
| Blood | 0.85 ± 0.08 | 1.92 ± 0.07 | ||||||||||
| Female | 47 | 23 (ranged 30–50 years) | Hair | 0.98 ± 0.03 | 1.78 ± 0.07 | |||||||
| Blood | 0.85 ± 0.04 | 1.79 ± 0.06 | ||||||||||
|
| ||||||||||||
| Ailin Falkmo Hansen | Norway | 2016 | Case-control | Both | 883 | 755 (65.2 ± 10.3) | Blood | 3.18 ± 5.26 | 3.47 ± 4 | ICP-MS | Mean of Hg in T2DM vs. controls | Age, sex, body mass index, waist-to-hip ratio, education, income, smoking, and family history of diabetes |
|
| ||||||||||||
| Yohei Hotta | Japan | 2018 | Case-control | Both | Group 1:71 | Group 1:12 (ranged 36–86 years) | Hair | 1.94 ± 2.11 | 2.88 ± 3.52 | ICP-MS | Mean of Hg in T2DM vs. controls | — |
| Group 2:92 | Group 2:33 (ranged 36–86 years) | 1.94 ± 2.11 | 1.98 ± 1.55 | |||||||||
|
| ||||||||||||
| Bert B. Little | USA | 2020 | Case-control | Both | 875 | 109 (55.0 ± 11.4) | Blood | 0.05 ± 0.27 | 0.12 ± 0.41 | NR | Mean of Hg in T2DM vs. controls | Age, gender, smoking tobacco, duration of residence, smelter worker, blood lead level, blood arsenic, cadmium level, gamma-glutamyl transpeptidase, hypertension |
| Muhittin A. Serdar | Turkey | 2009 | Case-control | Both | 53 | 31 (59 ± 9) | Blood | 1.53 ± 0.69 | 1.15 ± 0.54 | ICP-MS | Mean of Hg in T2DM vs. controls | — |
|
| ||||||||||||
| Xin Wang | USA | 2020 | Prospective cohort (17 years of follow-up) | Female | 1237 | 102 (50.0 ± 3.1) | Urine | 1.23 ± 1.31 | 1.08 ± 1.05 | ICP-MS | Mean of Hg in T2DM vs. controls | Age, race/ethnicity, study site, specific gravity, education, household income, body mass index, waist circumference, smoking, alcohol consumption, physical activity, energy intake, menopausal status, and use of the hormone, seafood, and rice intake |
|
| ||||||||||||
| NElL Io WARD | England | 1983 | Case-control | Both | 85 | 55 (59.7 ± 10.0) | Blood | 15 ± 5 | 12 ± 3 | Neutron-activation analysis (NAA) and electrothermal atomic absorption spectroscopy (EAAS) methods | Mean of Hg in T2DM vs. controls | — |
|
| ||||||||||||
| Iwona Rotter | Poland | 2015 | Case-control | Male | 313 | 55 (61.3 ± 6.3) | Blood | 4.53 ± 2.23 | 4.45 ± 1.58 | Inductively coupled argon plasma optical emission spectrometry (ICP OES) | Mean of Hg in T2DM vs. controls | — |
|
| ||||||||||||
| Junguo Zhang | China | 2021 | Case-control | Both | 15327 | 2132 (49.75 ± 17.88) | Blood | — | — | Inductively coupled plasma dynamic reaction cell mass spectrometry | OR for T2DM | Age, sex, hypertension, poverty-income ratio, education, marital status, and daily intakes of protein, total fat, sugar, fiber, total energy, alcohol, vitamin C, vitamin B6, selenium, calcium and omega-3 polyunsaturated fatty acid, moderate recreational activities, cotinine, and estimated glomerular filtration rate |
| KA HE | USA | 2012 | Prospective cohort (18 years of follow-up) | Both | 4163 | 288 (aged 20–32 years) | Toenail | — | — | Instrumental neutron-activation analysis | HR for T2DM | #Model 6: model 5 with additional adjustment for toenail selenium (quintiles) |
|
| ||||||||||||
| Tsung-Lin Tsai | Taiwan | 2019 | case-control | Both | 646 | 56 (55.37 ± 12.87) | Blood | — | — | Cold vapor atomic absorption spectrometry | OR for T2DM | Age, sex, BMI, education, hypertension, total cholesterol, fasting glucose, cigarette smoking, alcohol consumption, saltwater fish consumption, total calorie intake, protein and fat intake, geographical strata, seasonality, C-reactive protein, and hemoglobin |
|
| ||||||||||||
| S.-S. Moon | South Korea | 2012 | Case-control | Both | 3184 | 333 (58.8 ± 10.9) | Blood | — | — | Gold-amalgam collection method with DMA-80 | OR for T2DM | Adjusted for age, sex, region, smoking, alcohol consumption, and regular exercise |
|
| ||||||||||||
| DARIUSH MOZAFFARIAN | USA | 2013 | Prospective cohort (follow-up of 7.0 years) | Female | 9267 | 1010 (61.2 ± 8.9) | Toenail | — | — | Neutron-activation analysis | HR for T2DM | Adjusted for age, sex, race, region, month of toenail return, family history of diabetes, smoking status, BMI, hypertension, hypercholesterolemia, future cardiovascular disease case-control status (case or control), physical activity, alcohol use, and fish consumption |
| Male | ||||||||||||
|
| ||||||||||||
| S. Cordier | Canada | 2020 | Case-control | Both | 1874 | 217 (32.9 ± 4.8) | Blood | — | — | ICP-MS | OR for T2DM | Age, sex, waist circumference, smoking, omega-3 PUFAs |
|
| ||||||||||||
| Min Kyong Moon | South Korea | 2021 | Case-control | Both | 3787 | NR (aged ≥19 years) | Urine and blood | — | — | Amalgamation direct Hg analyzer | OR for T2DM | Age, sex, cigarette smoking, alcohol drinking, exercise, and education levels were included as covariates |
NR: not reported; T2DM: type 2 diabetes; OR = odds ratio; BMI = body mass index; ICP-MS: inductively coupled plasma mass spectrometry; PUFAs: Polyunsaturated fatty acids.
Figure 2Forest plot for the mean levels of Hg in patients with T2DM compared with healthy controls stratified by the type of sample.
Figure 3Forest plot for the association between Hg and risk of T2DM stratified by the type of sample.
Figure 4Forest plot for the association between Hg and risk of T2DM stratified by the study design.
Figure 5Forest plot for the association between Hg and risk of T2DM stratified by the sex of participants.
Figure 6Forest plot for the association between Hg and risk of T2DM stratified by the method used for the measurement of Hg. ICP-MS: inductively coupled plasma mass spectrometry, NR: not reported, NAA: neutron-activation analysis, CVAAS; cold vapor atomic absorption spectrometry.
Figure 7Funnel plot for publication bias in studies exploring the relation of Hg to the risk of T2DM.