Characteristics of included studies

We identified 178 citations through the PUBMED, 255 through EMBASE database search and two citations through searching registries. We screened all retrieved citations, and after excluding duplicates, a total of 430 studies were eligible for full-text screening. In all, 13 studies were included in the qualitative synthesis, whereas eleven studies were included in the meta-analysis (Fig. 1). Notably, the included studies were all cross-sectional studies comprised of studies from 3 different countries, which included; Turkey (n = 9)(16,17,18,19,20,22,23,24), Poland (n = 1) [21], Malaysia (n = 1) [32], China (n = 1) [33], and Brazil (n = 1) [23] (Table 1). The meta-analysis comprised of 814 patients with obesity and 598 non-obese individuals. Only six studies(17,18,19,20,21,22); reported on the mean age and 6 studies [18,19,20,21,22,23] reported on the mean BMI of obese participants (Table 2). The mean age of the included obese participants was 44.03 ± 18.01, while the mean age of the control participants with normal body weights was 41.67 ± 17.93. Overall, the individuals with obesity were older (mean difference: 1.05; 95%CI: 0.28 to 1.82; p < 0.01) with higher SBP (MD: 6.38; 95%CI: 3.64 to 8.07) and DBP (MD: 2.37; 95%CI: -0.36 to 4.88) when compared to controls. The characteristics of the included studies are presented in Table 1.

Fig. 1
figure 1

PRISMA flow diagram illustrating the study selection process

Table 1 Characteristics of studies reporting on the mean platelet volume in individuals with obesity (n = 13)
Table 2 Cardiovascular-risk profile of included participants

Assessment of publication bias

There were 11 studies included in this meta-analysis, and visual inspection of the funnel plots indicated asymmetry. The contour-enhanced funnel plot was used to distinguish between publication bias and alternative sources of asymmetry. The funnel plot revealed that small studies were not only found in the regions of no statistical significance (p > 10%), but they also fell in the region of statistical significance (p < 5%). This suggests that asymmetry was a result of alternative sources but not solely a result of publication bias. In addition, we performed the Egger’s regression test to explore the association between the reported effect size and the study size. In addition, there was no evidence of small study effects (p = 0.34) (Supplementary table 4S) or publication bias by the Egger test (bias: 0.17; p = 0.51).

Quality assessment of the included studies

The Newcastle–Ottawa scale was used to assess the risk of bias in the included studies (Fig. 2B). In all, 42% of the included studies [15, 17, 18, 22, 34] scored as high-quality (scores > 7), and an equal proportion of 42% of the studies [16, 19, 20, 23, 32] were of moderate-quality (scores ranging from 5–6). While, only 16% were considered as having a low-quality [21, 24] and had scored lower than 5 (Table 2S).

Fig. 2
figure 2

Publication and risk of bias assessment. A shows the funnel plots, and. B demonstrates the overall risk of bias

Primary findings

A total of 7 studies reported increased MPV levels in children [15, 22] and adults with obesity [16,17,18, 21, 23]. Only three studies reported findings suggesting that the MPV remains unchanged in obesity [20, 24, 35]. Notably, the included studies reported an inverse [20] and a direct association [16,17,18, 23] between BMI levels and the MPV in individuals with obesity. Moreover, the MPV is also inversely associated with HDL cholesterol levels and platelet counts in individuals with obesity. The primary outcome of this meta-analysis included changes in the mean platelet volume in 814 individuals with obesity reported in 10 studies [15,16,17,18,19,20, 22,23,24, 36]. After pooling the effect estimates, we showed that the mean platelet volume significantly increased in individuals with obesity compared to non-obese individuals (MD 0.79; (95%CI: 0.42 to 1.16). However, the levels of statistical heterogeneity were high (I2 = 93.44%), which were unexplained by the test for subgroup analysis based on the risk of bias in the included studies (Fig. 2).

Secondary findings

In assessing the secondary outcome of the meta-analysis which focused on the ASCVD-risk in individuals with obesity. Only 5 (50%) of the included studies reported on fasting blood glucose levels [17,18,19,20, 22], and 6 studies [16,17,18,19,20,21,22] lipid profiles in individuals with obesity. As expected, our meta-analysis showed that individuals with obesity had higher; fasting blood glucose levels (MD:2.75; 95%CI, 1.55 to 3.94; I2 = 76%,p = 0.02); total cholesterol (SMD: 0.14 [95%CI: 0.03 to 0.25], I2 = 72%, p = 0.04); LDL cholesterol (SMD: 0.22[95%CI: -0.03 to 0.47], I2 = 76%, p = 0.009) and triglycerides (SMD: 0.43[95%CI:0.18 to 0.68], I2 = 75%, p = 0.0007) (Table 2).

Subgroup and sensitivity analysis

There were substantial levels of statistical heterogeneity in the reported effect estimates. We, therefore, performed a subgroup analysis to explore the sources of heterogeneity based on the differences in the risk of bias in the included studies (Fig. 3). Although the small number of studies and covariance because of an unequal number of studies in each subgroup, the test for subgroup effect showed a significant subgroup effect (p = 0.19). This suggests that the reported differences in MPV may be influenced by the risk of bias in the included studies.

Fig. 3
figure 3

Forest plot showing the Random-effects pooled mean difference of the mean platelet volume in obese and non-obese individuals

Notably, the forest plot (Fig. 3) demonstrates that the studies with high-quality showed a more significant mean difference (MD) in the MPV of individuals with obesity (MD: 1.01 [0.39 to 1.62]], I2 = 94.1%) when compared to studies with a moderate-quality (MD: 0.44 [0.19 to 0.70], I2 = 56.9%). In comparison, studies with a low-quality showed the highest increase in MPV in individuals with obesity compared to controls (MD: 1.22 [-0.49 to 2.92], I2 = 93.3%). Notably, amongst the studies reporting on a cohort of individuals with obesity from the Middle East, all studies were conducted in Turkey, and only 28.6%(n = 2) of the studies were moderate-quality. While the majority, 71% (n = 5) of the studies had a low-quality of bias (Table 1). Unlike the studies from Brazil and Poland, which were both of moderate-quality. The sensitivity analysis showed that studies with a low-quality overestimated the effect size of the primary outcome (0.98[0.33 to 1.62], I2 = 92.15%, p < 0.001) when compared to studies with a moderate-quality of bias (0.25[0.01 to 0.48], I2 = 62.06%, p < 0.041]. We also explored whether the variations in the included participants’ age modified the effect estimate of the primary outcome (supplementary table 2S). Two of the included studies reported on the MPV of children (< 18 years of age) [15, 22], while the majority of the studies reported on cohorts comprised of adults (> 18 years of age) [16,17,18,19,20,21, 23, 24].

Meta-regression analysis

We explored the associations between the markers of ASCVD-risk reported pooled mean platelet volume estimates. In the reported markers, collinearity existed between the BMI, systolic blood pressure, diastolic blood pressure and fasting blood glucose levels. So, the explanatory variables selected for the meta-regression model included Age, BMI, platelet counts, cholesterol and Triglyceride levels. Notably, the age of the study participants was reported in 8 studies [17,18,19,20,21,22], and BMI levels were reported in 7 studies [6,7,8,9,10,11,12]. Whereas only six studies [16,17,18,19,20,21,22] reported on the total cholesterol levels (Table 2).

In the meta-analysis, the levels of MPV were significantly elevated in obesity with moderate levels of certainty (Table 3). Interestingly, the meta-regression showed that age, BMI, and total cholesterol levels are significant confounders of the reported differences in changes in MPV in individuals with obesity (Table 2). While the inverse association between the BMI and MPV (Coefficient: -0.57, standard error (SE): 0.18, p < 0.001) is congruent with the previous study by Esen et al. [20], these contradict the positive associations reported in four of the included studies [16, 17, 22, 23]. Furthermore, the meta-regression showed a significant direct association between the differences in triglyceride and MPV levels (Coefficient: 4.99, standard error (SE): 1.14, p < 0.001).

Table 3 Summary of findings

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