The pooled prevalence for urinary tract infection came out at 16.8 percent, with a 95 percent confidence interval stretching from 7.4 percent to 33.7 percent. For genital tract infection, the pooled estimate was 22.8 percent, with a confidence interval of 12.6 percent to 37.5 percent. Those intervals are strikingly wide, and they reflect a deeper problem: the individual studies behind them disagreed with one another to an extraordinary degree. Statistical heterogeneity, measured by the I² statistic, ranged from 95.0 percent to 96.7 percent across the pooled outcomes, meaning that almost none of the variation between studies could be attributed to chance alone. In practical terms, the studies were not merely sampling from slightly different populations; they were measuring different things in different ways.

To grapple with that uncertainty, the authors went beyond the conventional confidence interval and calculated 95 percent prediction intervals, which attempt to describe the range of prevalence a brand-new study, conducted in a new setting, might plausibly report. For urinary tract infection, that prediction interval ran from 1.4 percent to 74.7 percent. For genital tract infection, it spanned 3.4 percent to 71.5 percent. The message is unambiguous: a clinician reading this review should not assume that the pooled figure of 16.8 or 22.8 percent applies neatly to their own clinic. Depending on the infection category, the patient population, and the diagnostic methods in play, the real-world prevalence could be nearly negligible or affect three out of four women.

The methodological machinery behind the estimates deserves attention, because it represents current best practice in meta-analysis of proportions. The researchers searched five electronic databases from their inception through 28 February 2026, identifying observational studies that reported infections among women with gestational diabetes. They pooled proportions on the logit scale, a transformation that keeps estimates within the bounds of zero and one hundred percent, using random-effects models fitted with restricted maximum likelihood estimation. Rather than relying on standard confidence interval formulas, which can perform poorly with proportions, they employed modified Hartung–Knapp intervals, an approach designed to better account for uncertainty in between-study variance. They also evaluated the certainty of the evidence using the GRADE framework, and rated it as low for both pooled outcomes, a judgment that reflects the observational design of the underlying studies and their pronounced inconsistency.

One of the most intriguing findings emerged from an exploratory meta-regression: larger studies tended to report lower prevalence. The regression coefficient was −0.809, with a 95 percent confidence interval of −1.281 to −0.337 and a P value of 0.001. Even after accounting for study size, however, residual heterogeneity remained stubbornly high, at I² equal to 96.3 percent. The pattern is suggestive of small-study effects, a phenomenon in which smaller studies, which are more vulnerable to publication bias and selective reporting, tend to produce more extreme estimates. It could also reflect genuine differences in the settings where smaller studies are conducted, such as single hospitals with selected patient populations, compared with larger multicenter investigations that capture a broader cross-section of pregnant women.

The spectrum of infections covered by the review extends beyond the urinary and genital tracts. Three studies examined periodontal infections, but because they were too few and too dissimilar to pool statistically, the authors summarized them narratively rather than forcing them into a meta-analytic framework. That decision highlights a recurring theme in the review: the fragmented nature of infection research in this population. Studies have examined different infection sites, recruited patients from different clinical settings, and applied different diagnostic criteria, making apples-to-apples comparisons nearly impossible. Bacterial vaginosis, vulvovaginal candidiasis, and group B Streptococcus colonization all fall under the genital tract umbrella, yet each is diagnosed and treated differently, and few studies disaggregated their results by these subcategories.

Why does infection matter so much in gestational diabetes? The physiological logic is well established. Elevated blood glucose levels provide a favorable environment for bacterial and fungal growth, and hyperglycemia impairs immune cell function, diminishing the ability of neutrophils and other defenders to contain invading pathogens. Pregnancy itself compounds the risk, as hormonal changes alter the urogenital microbiome and the growing uterus impedes urinary drainage, creating conditions favorable to ascending infection. When these two risk factors converge, the potential for maternal morbidity rises, and infections during pregnancy are linked to complications ranging from preterm birth to neonatal intensive care admission. Crucially, the review’s authors frame infections as a potentially preventable cause of harm, which is precisely why accurate prevalence estimates matter: you cannot design screening or prevention programs for a risk you cannot measure.

The review’s conclusions are deliberately restrained. The authors describe their work as providing category-specific estimates and an evidence map for infection research in women with gestational diabetes, rather than definitive clinical benchmarks. They emphasize that the wide prediction intervals mean the estimates must be applied in the context of the specific infection category and clinical setting. Their prescription for the field is equally clear: standard definitions of infection outcomes and multicenter cohorts are needed to obtain more precise estimates that can actually inform clinical practice. Until such studies exist, the pooled figures should be treated as provisional signposts rather than reliable point estimates, and clinicians should remain alert to local patterns of infection among their own patients with gestational diabetes.

For the global health community, the stakes are considerable. Gestational diabetes affects a substantial and growing share of pregnancies worldwide, driven by rising maternal age, obesity, and sedentary lifestyles, and the burden falls disproportionately on low- and middle-income countries where diagnostic resources may be limited. If infections among these women are as common as the upper bounds of the prediction intervals suggest, the cumulative burden of preventable maternal illness could be substantial. This review does not settle the question, but it does something arguably more valuable: it maps the terrain, quantifies the uncertainty, and shows future researchers exactly where better data are needed. In a field where the evidence has been scattered across dozens of small, heterogeneous studies, that map may prove to be the study’s most enduring contribution.

Subject of Research: Prevalence of infections among women with gestational diabetes mellitus

Article Title: Prevalence and spectrum of infections among women with gestational diabetes mellitus: a systematic review and meta-analysis

Article References: Li, M. W., Liu, X. M., Li, L. J., & Jiang, W. H. (2026). Prevalence and spectrum of infections among women with gestational diabetes mellitus: a systematic review and meta-analysis. BMC Infectious Diseases. https://doi.org/10.1186/s12879-026-14484-z

Image Credits: AI Generated

DOI: 10.1186/s12879-026-14484-z

Keywords: gestational diabetes mellitus, urinary tract infection, genital tract infection, meta-analysis, systematic review, prevalence, heterogeneity, pregnancy, maternal morbidity, GRADE, prediction intervals, BMC Infectious Diseases