Table 2.
Data source (Author, country, year) | Population | Study | Adiponectin | MetS | Risk assessment | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Base-line Features | Age (year) | Gender (F/M) | Follow up (year) | Category | Sample | Measurement | Definition | Incidence (N, %) | Indicator | Comparison (μg/ml) | Extent | Methods | Adjusted covariates | |
Seino et al., Japan, 2009 | 1. General population 2. Without MetS, endocrine disease, renal or hepatic disease 3. Without medication for diabetes |
MetS: 46.6 ± 9.2 |
0/416 | 6 | Total | Serum | ELISA | Japanese criteria | 27, 6.4 | OR | T1 vs. T3 (≥7.44 vs. ≤ 4.65) | 0.45 0.17–1.19) |
Chi-square | NA |
Non-MetS: 44.1 ± 8.3 |
HMW | HR | ≤ 2.65 vs. >2.65 | 1.56 (1.05–2.29) |
Cox hazard model | Age and BMI | ||||||||
OR | T1 vs.T3 (≥5.07 vs. ≤ 3.16) | 0.24 (0.08–0.75) |
Chi-square | NA | ||||||||||
Nakashima et al., Japan, 2011 | 1. General population 2. without MetS and DM | Non-MetS (M): 61.2 ± 16.0 |
312/224 | 3.2 | Total | Plasma | ELISA | AHA/NHLBI | M: 46,20.5 | HR | M: SD (5.7) incre-ment vs. before | 0.68 (0.51–0.92) |
Cox hazards model | NA |
Non-Mets (F): 59.8 ± 13.2 |
F: 43, 13.8 | HR | F: SD (6.6) incre-ment vs. before | 0.63 (0.46–0.87) |
Cox hazards model | NA | ||||||||
Men MetS (M): 60.8 ± 14.8 |
HMW | HR | M: SD (4.3) incre-ment vs. before | 0.69 (0.51–0.93) |
Cox hazards model | Age, BMI, OGTT, and HOMA-IR | ||||||||
MetS (F): 63.3 ± 10.4 |
HR | F: SD (5.5) incre-ment vs. before | 0.70 (0.51–0.96) |
Cox hazards model | Ditto | |||||||||
Juonala et al., Finland, 2011 | 1. General population 2. without MetS |
Non-MetS: 31.5 ± 5.0 |
659/829 | 6 | Total | Serum | RIA | NCEP-ATPIII | M: 102, 12.3 | OR | 1 unit increment vs. before | 0.94 (0.90–0.99) |
Logistic regression | Age, sex, BMI, LDL-C, CRP, |
MetS: 33.0 ± 4.7 |
F: 133, 20.2 | leptin, insulin, smoking, family history of CVD, WC, HDL-C, TG, SBP and FBG | ||||||||||||
Kim et al., Korea, 2013b | 1. General Population 2. without any MetS components 3. without CAD history |
MetS (M): 56.0 ± 8.1 |
1,231/831 | 2.6 | Total | Serum | RIA | Modified NCEP-ATP-III | M: 153, 18.4 | OR | M: Q4 vs. Q1 (>11.22/ < 5.94) | 0.25 (0.14-0.47) |
Logistic regression | Age, BMI, LDL-C, smoking, exercise, CRP, HOMA-IR and TG. |
Non-MetS (M): 56.6 ± 8.2 |
F: 199, 16.2 | OR | M: 5 unit incre-ment vs. before | 0.82 (0.73–0.92) |
Logistic regression | DITTO | ||||||||
MetS (F): 55.4 ± 7.8 |
OR | F: Q4 vs. Q1 (>15.24/ < 8.91) | 0.45 (0.28–0.74) |
Logistic regression | DITTO | |||||||||
Non-MetS (F): 52.5 ± 7.9 | OR | F: 5 unit incre-ment vs. before | 0.90 (0.83–0.96) |
Logistic regression | DITTO | |||||||||
Hata et al., Japan, 2015 | 1. General population 2. Without MetS |
40.4 ± 0.5 | 0/365 | 3.1 | Total | Serum | RIA | IDF | 45, 12.3 | OR | Q4 vs Q1 (≥8.9 vs. ≤ 4.9) | 0.14 (0.04–0.48) |
Chi-square | NA |
Lindberg et al., Denmark, 2017 | 1. General population 2.Without MetS, CVD, DM 3. Without medication on MetS |
Q1:42 ± 8 | 406/747 | 9.4 | Total | Plasma | Immuno-fluoro-metric assay | NCEP-ATP-III criteria | 187,16.2 | OR | Q1 vs. Q4 (≤ 6.6 vs. >12.5) | 2.24 (1.11–4.52) |
Logistic-regression | Age, gender, smoking, physical activity, SBP, DBP, FBG, BMI, TC, HDL, LDL, and eGFR. |
Q2:44 ± 8 | OR | Per 1 unit decre-ment vs. before | 1.56 (1.10–2.23) |
Logistic-regression | DITTO | |||||||||
Q3:45 ± 9 | ||||||||||||||
Q4:49 ± 8 |
BMI, body mass index; CRP, C-reactive protein; CVD,cardiovascular disease; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; ELISA, enzyme-linked immunoabsorbent assay; F, female; FBG, fasting blood glucose; HDL-C, high density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; HR, hazard ratio; LDL-C, low density lipoprotein cholesterol; M, male; MetS, metabolic syndrome; NA, not available; OGTT, oral glucose tolerance test; OR, odds ratio; Q, quantile; RIA, radioimmuno assay; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; WC, waist circumference.