Cross-sectional design precludes causal inference and temporal ordering

The gap

Evidence profile

Sourced from the limitations section and limitations and future work of the source papers, classified as general, drawn from work published between 2025 and 2026, spanning 3 journals. Those papers have been cited 1 times in total.

Supporting evidence — 3 representative gaps

• Sociodemographic, Lifestyle, and Social Isolation Correlates of TyG, METS-IR, and SPISE-IR Scores in a Large Spanish Working Population (2025) · Medical Sciences · cited 1× · doi

  cross-sectional design precludes causal inference and temporal ordering; longitudinal follow-up is needed to establish whether social isolation and lifestyle factors precede changes in IR indices and incident diabetes - residual confounding is possible; observational analyses cannot rule out all confounders - self-report for lifestyle and social isolation may introduce misclassification - the use of the IPAQ-SF represents a methodological limitation, as it reflects only the previous seven days and may be influenced by temporary circumstances, failing to capture long-term habitual activity - laboratory variability could introduce non-differential errors - index definitions and cut-offs: although TyG, METS-IR, and SPISE-IR are validated surrogates, thresholds may vary by population - potential selection bias if workers with worse health were less likely to attend screening - lack of direct insulin measures limits physiologic granularity

  generallimitations section
  Keywords: cross-sectional design precludes causal inference temporal ordering longitudinal
• Correlation of complete blood count parameters with glycemic control in type 2 diabetes mellitus (2026) · Romanian Journal of Medical Practice · doi

  The current study has several limitations which should be considered with due attention. The first limi- tation is the small sample size (n = 50), which by itself interferes with the statistical power of our analyses and increases the risk of both type 1 errors (false positives due to chance findings), and type 2 error (false nega- tives due to insufficient power). There was no formal calculation of power and therefore, the results ought to be considered as exploratory. Second, the cross-sec- tional design does not allow us to establish a causality; we only report relationships but not predictive relation- ships. The research would need to be conducted in a longitudinal study to determine whether the hemato- logical parameter changes happen before or after the change in the glycemic control. Third, despite the use of exclusion criteria, it is possible that undetermined of- fending factors, including nutritional status (e.g., iron, vitamin B12, and folate levels), smoking history, and physical activity levels, use of certain oral hypoglycemic agents or insulin regimens, and presence of undeter- mined subclinical conditions could have affected hema- tological parameters and glycemic control [24]. Finally, given the exploratory nature of this study, several statistical comparisons were performed with- out adjusting them formally adjustment multiple test (such as the Bonferonni correction), which increases the probability of type I error. Therefore, the results particularly those of borderline significance must be in- terpreted with caution, and require replication in inde- pendent cohorts [25]. CONCLUSION In this exploratory study, patients with poor glyce- mic control exhibited significantly higher values of WBC, neutrophil, and platelet counts, as well as, an in- creased MPV, compared to the good glycemic control group. Notably, platelets count showed moderate posi- tive correlations with the levels of HbA1c. On the other hand, RBC indices were statistically similar across the cohorts. These findings suggest that platelet activation and severity of glycemic dysregulation in T2DM are as- sociated with inflammatory markers. The results sug- gest a relationship between inflammatory and throm- bocytic parameters and glycemic control status, but the small sample size of the study, the use of cross-section- al design, and the likelihood of unmeasured confound- ing lead to the conclusion that the results are hypothe- sis-generating as opposed to conclusive. To confirm such associations, clarify the underlying processes, de- fine the temporal relation, and also if these easily ob- tainable hematological measures have clinical value in the management of diabetes mellitus or whether they are useful in stratifying risk of diabetic complications, larger, prospective cohort studies are necessary.

  generallimitationsevidence 5/5
  Keywords: glycemic control power type exploratory levels several considered small sample size statistical increases risk false
• Hypoglycemia and Long-Term Cardiovascular Outcomes in Diabetes Mellitus: A Systematic Review of Prognostic Evidence (2026) · Cureus · doi

  Future research should move beyond secondary and post hoc analyses and prioritize prospective studies in which hypoglycemia is explicitly treated as the primary exposure of interest rather than a treatment-related byproduct. Such studies would benefit from more precise characterization of event timing, severity, recurrence, and clinical context [18]. The integration of continuous glucose monitoring data is likely to be especially valuable, as it would permit more accurate quantification of hypoglycemic burden, nocturnal episodes, and glycemic variability, all of which may influence cardiovascular risk but are incompletely captured by conventional reporting methods. Further work is also needed to clarify threshold effects, including whether risk is driven predominantly by severity, frequency, or a combination of both, and to identify particularly vulnerable phenotypes such as patients with heart failure, chronic kidney disease, autonomic neuropathy, or established atherosclerotic cardiovascular disease [20]. Importantly, future studies should aim to distinguish more clearly between hypoglycemia as a causal trigger and as a prognostic marker through robust adjustment strategies, advanced causal inference methods such as marginal structural models or mediation analyses, and, where feasible, interventional designs. A more refined understanding of these subgroups may help determine whether hypoglycemia functions primarily as a modifiable trigger, a prognostic marker, or both.

  generalfuture workevidence 5/5
  Keywords: hypoglycemia future analyses severity cardiovascular risk whether disease causal trigger prognostic marker move beyond secondary

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