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For librarians. For Reviewers. Co publisher. Privacy statement. Downloads Download data is not yet available. Metrics Metrics Loading A Watila MM, Nyandaiti YW, Ahidjo A, Balarabe SA, Ibrahim A, Bakki B, et al. The descriptions of original studies were summarized using Tables and forest plot.

The MedCalc — software was used for data entry and analysis. The random effect model was used to estimate the pooled correlation considering any heterogeneity inherent in the meta-analysis.

We also performed robustness analysis to assess the impact each study on the pooled result by removing one at a time from the analysis for all studies.

Of the total, were excluded after reviewing titles, abstracts and full text and applying the inclusion criteria. Fourteen studies were included in systematic review. From these 14 articles, only 11 of them were incorporated in meta analysis Fig.

All of the included articles had performed comparisons between fasting and postprandial blood glucose with HbA1c.

Data from a total of and diabetic patients were included in this systematic review and meta-analysis, respectively. The included articles had study population varied from 50 Gupta et al. Mean age of the study subjects ranged from While eleven studies were done in type 2 diabetic patients exclusively, the rest 3 studies included both type 1 and type 2 diabetic patients.

Some of the studies Schernthaner et al. Two studies, Woerle et al. HbA1c was measured using ion exchange high-performance liquid chromatographic techniques in all included studies except two studies, Gupta et al.

Six of the included studies used two blood samples each for FPG and 2hPPG determination taken before and after 2 h of breakfast, respectively. The other five studies employed multiple plasma glucose measurements per day before and after each meal breakfast, lunch and dinner while Saeed and Haddadihneshad et al.

But Shrestha et al. From these, seven studies found a better correlation between PPG and HbA1c than FPG. In contrast to this, the other three studies revealed a stronger correlation between FPG and HbA1c than PPG.

The remaining one study found almost equal correlation coefficients for both tests. The correlation coefficient r ranged from 0. Alternatively, Woerle et al. A decrease in PPG was accounted for nearly twice as much as FPG did for the decreases in HbA1c. On the other hand Schernthaner et al.

The authors stated that PPG play a major role in patients suffering from mild or moderate hyperglycemia while FPG appears as a main contributor to the overall diurnal hyperglycemia in poorly controlled diabetic patients.

Similar suggestions were also made by Monnier and his co-authors. Of these four studies, three of them found better sensitivity, specificity and positive predictive value for PPG than FPG Table 2. This indicates that PPG is more sensitive, more specific and has a higher predictive value than FPG.

However, these studies used a different cut of value for both FPG and PPG. Datta et al. Generally in all of the three approaches or methods, PPG was found to be strongly correlated or to be a better parameter in predicting or achieving target HbA1c values than FPG.

Summary of the results showed that PPG has a better correlation with HbA1c, superior accuracy sensitivity and specificity to predict HbA1c values and greater contributions to achieve target HbA1c values than FPG.

Meta-analysis was done on eleven selected studies listed in Table 3. Seven of the studies included in meta-analysis found better correlations between 2hPPG and HbA1c while the remaining 3 studies reported better correlation between FPG and target HbA1c values as shown in Table 3.

Based on meta-analysis of these 11 included studies the pooled correlation coefficient was 0. Forest plot for correlations of FPG and 2hPPG with HbA1c is shown in Figs. Forest plot of the 11 studies that quantitatively assessed the correlation between HbA1C and FPG represented by the random effect model.

Forest plot of the 11 studies that quantitatively assessed the correlation between HbA1C and PPG represented by the random effect model. The use of different cut off points for defining good glycemic control affects the sensitivity, specificity and positive predictive values of FPG and PPG tests.

The 11 studies included in meta-analysis showed high heterogeneity according to Cochrane Q and I 2 test statistic. We also performed robustness analysis by revolving one article at a time. But each study did not bring significance difference on the final pooled correlation coefficient.

Historically, glycemic control efforts have emphasized on achievement of HbA1c and FPG targets [ 31 ]. Unfortunately majority more than two thirds of patients in therapeutic aims targeting for HbA1c and FPG has failed to achieve their glycemic goals [ 1 , 32 ].

On the other hand, HbA1c has some important limitations and is a rather complex measure of hyperglycemia. A large number of medical condition such as the presence of hemoglobin variants, malignancies, hemolytic anemia, and variety of systemic conditions as well as various medications and pregnancy are associated with alterations in the HbA1c values and may provide unreliable information [ 33 , 34 ].

Apart from these factors, HbA1c also neither captures glucose fluctuations over short period of time nor provide any information on glucose dynamics [ 18 ]. But these glycemic variability are critical for safe and timely treatment adjustment and clinical decision makings [ 35 ].

As a result, there has been increasing interest in additional markers for better glycemic control over shorter timeframes [ 18 ]. In this review we identified 14 articles that equated the relationship between short term glucose measures, FPG and 2 h PPG, and long term glycemic indicator HbA1c.

According to this review and meta-analysis a better correlation was found between 2 h PPG and HbA1c than FPG pooled correlation r 0. This means that patients who achieved 2 h PPG within the reference limit will better accomplish target HbA1c values than patients realized FPG within recommended range.

Three out of four studies also found a better sensitivity, specificity, and positive predictive value for PPG. This can be interpreted as when PPG is high or its control is poor it is more likely to get high HbA1c above the recommended range. Our review shad that in all the circumstances PPG is either a better correlate or accurately predicts HbA1c value or its contribution to the overall hyperglycemia is greater than FPG signifying that control of postprandial hyperglycemia is essential for achieving recommended HbA1c goals.

Growing body of evidences have also shown a strong association between PPG and cardiovascular risk and outcomes [ 36 ], oxidative stress, carotid intimal thickness and endothelial dysfunction [ 37 ].

A recent diabetes complications trial study concluded that PPG, but not FPG, was an independent predictor of mortality and cardiovascular complications in diabetes [ 36 , 38 , 39 ].

It is also plausible that humans spend half of their lives in postprandial states and thus, to achieve better long-term metabolic control HbA1c and minimize the risk of chronic diabetic complications, glucose monitoring in postprandial state will be indispensable.

Considering the biochemical and pathological differences in the two types, a distinct approach would be appropriate to characterize these associations in type 1 diabetic patients. Regarding day to day variations numerous evidences suggested that several determination of glucose over a period of several weeks would be better indicator of glycemia and then better correlated with HbA1c than single or few measurements in single day.

But in this review only three studies Saeed, ; Haddadihneshad et al. All others were based on either glucose data generated on single day or single glucose measurement. None of the included studies identified the separate effect of age and sex on the association between these different plasma glucose profiles and HbA1c values.

However Szoke et al. found that older people have more postprandial hyperglycemia than younger people [ 40 ]. Finally significant heterogeneity was found in the pooled correlation estimates of both FPG and PPG. Even though we attempted to explain it through the random effect regression model, many factors possibly contributing to this residual heterogeneity could not be assessed because they were not reported in most studies.

For example, duration of diabetes and type of treatments are likely to have an important effect on correlation results. Finally it should not be also ignored that methodological differences may contribute to the study to study variation. It is suggested that the multiple regression analysis used for studying the relationship between A1C and glucose values at different times is an unstable model when explanatory variables, i.

Inclusion of different studies from different geographical location, treatment group as well as from different methodological approach would be problematic to end up with particular inferences. Lack of uniform cut off points for good glycemic control across the studies disallowed us to pool the specificity and sensitivity of FPG and PPG in predicting HbA1c values.

And also due to incomplete data on inclusion criteria, duration of diabetes, age groups and others it was difficult to perform subgroup analysis.

Another constraint in this meta analysis could arise from publication bias as studies that have negative result are less likely to be published. The result of our reviews showed that PPG strongly correlate with HbA1c or contributes significantly to overall glycemic control.

This is in line with contemporary evidence that showed strong relation between PPG and development of diabetes complications. Consequently we in a position to claim that special attention should be given to monitoring and treating PPG until the ongoing debate are resolved through large randomized control trials.

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