The FSFI has been a workhorse of sexual medicine since its introduction in 2000. The 19-item self-report questionnaire assesses six domains of sexual function over the previous four weeks: desire, arousal, lubrication, orgasm, satisfaction, and pain. Respondents rate each item on a scale, and the domain scores are typically summed into a total score, with a widely used clinical cutoff distinguishing women with and without sexual dysfunction. The instrument was developed and validated in general community and clinical samples of adult women, however—not in people navigating the profound hormonal, anatomical, and relational upheavals of pregnancy and the postpartum period. Whether its scores mean the same thing in those populations had remained largely an open question.

To address it, the research team adopted a contemporary, argument-based approach to validity, guided by modern frameworks that treat validity not as a property of the test itself but of the interpretations and decisions built on its scores. Rather than asking simply whether the FSFI is valid, the researchers examined whether specific claims about what FSFI scores mean in perinatal samples are supported by evidence. This matters because perinatal sexual function is not simply typical sexual function in a different context. Hormonal shifts, tissue changes from delivery, breastfeeding, fatigue, and the psychological transition to parenthood all reshape sexual response in ways that a questionnaire designed for other populations may or may not capture.

The centerpiece of the investigation was a series of factor analyses, statistical techniques that test whether the pattern of people’s answers matches the structure the questionnaire assumes. The results offered a clear verdict on one front: the six-domain structure of the FSFI—desire, arousal, lubrication, orgasm, satisfaction, and pain—was supported in sexually active pregnant and postpartum individuals. In other words, the questionnaire’s underlying architecture holds together in this population, and each domain appears to tap a distinct, coherent aspect of sexual function. That finding gives researchers license to continue using and interpreting the six domain scores with reasonable confidence in perinatal research.

The same could not be said for the total score. Models that assumed a single overarching dimension of sexual function underlying all the items—essentially, the assumption baked into summing everything into one number—were not supported by the data. This is a technically significant result. When a unidimensional model fails, it means the domains are not interchangeable reflections of one common construct, and a total score risks blending genuinely different phenomena into a single, potentially misleading figure. A person might score low overall because of pain alone, for example, while their desire, arousal, and satisfaction remain intact—clinically meaningful distinctions that a total score would obscure. The authors’ conclusion on this point is unambiguous: the use of an FSFI total score is not supported in pregnant and postpartum samples.

Item response theory analyses added a second layer of nuance. These models examine how well each individual item distinguishes between people at different levels of the underlying trait. The findings showed that FSFI items discriminated well at low-to-average levels of sexual function but performed less effectively at higher levels. In practical terms, the questionnaire is most informative for pregnant and postpartum individuals who have poorer sexual function or who are experiencing sexual difficulties. For people functioning well, the items become less precise, meaning small differences among high-functioning individuals may not be reliably captured. This has a silver lining for clinical and research applications focused on identifying and understanding sexual problems, but it cautions against over-interpreting fine-grained differences among people reporting generally healthy sexual function.

The researchers also examined how FSFI domain scores relate to external variables, a standard strategy for testing whether a measure behaves the way theory says it should. With a few exceptions, this evidence indicated that the domain scores are sensitive to real individual differences in sexual function and can distinguish between individuals with and without distressing sexual difficulties. That is, the domains correlate with other measures in sensible ways and show the kind of clinical sensitivity that a useful assessment tool requires. Two domains, however, showed weaker performance: sexual arousal and sexual satisfaction. The authors advise that results from these two domains, like the total score, be interpreted with particular caution in perinatal samples until further evidence accumulates.

The stakes of this psychometric housekeeping are higher than they might appear. Perinatal sexual dysfunction is associated with depression symptoms, relationship dissatisfaction, and distress for both members of a couple, and researchers have increasingly documented trajectories of sexual well-being across the transition to parenthood. Interventions are being developed to support couples’ sexual health during this period, and clinical conversations about resuming intercourse after childbirth depend on accurate assessment. If the field’s dominant measurement tool were quietly mismeasuring these constructs, studies could reach distorted conclusions, clinical cutoffs could misclassify people, and patients’ experiences could be misunderstood. This study provides a foundation of evidence showing which parts of the instrument can be trusted in this population and which cannot.

The work also exemplifies a broader shift in how psychological and sexual health measures are evaluated. Older validation studies often reported a handful of statistics and declared an instrument valid or invalid. Contemporary validity theory, by contrast, builds an integrated argument, weighing evidence from internal structure, item-level performance, and relations to external variables to evaluate specific claims about score interpretation. By applying this framework to the FSFI, the researchers modeled a more rigorous standard for sexuality research, where measurement quality has historically received less scrutiny than in other areas of psychology. The study’s data and analysis code were also made publicly available through the Open Science Framework, supporting transparency and reuse.

For now, the practical guidance is clear. Researchers studying pregnancy and the postpartum period can continue to use the FSFI’s six domain scores as meaningful indicators of the underlying constructs, and the measure appears well suited to identifying individuals with sexual difficulties. But the familiar habit of reporting a single total FSFI score should end in perinatal research, and the arousal and satisfaction domains warrant cautious interpretation. The authors note that further research is needed to address the limitations identified, including the weaker performance at higher levels of function. Until that evidence arrives, this study offers the field something it has long lacked: a clear, evidence-based map of what one of sexual medicine’s most widely used questionnaires can—and cannot—reliably tell us about sexual health during one of life’s most transformative transitions.

Subject of Research: Validation of the Female Sexual Function Index for measuring sexual function in pregnant and postpartum individuals

Article Title: A Validation Study of the Female Sexual Function Index for Use in Pregnant and Postpartum Samples

Article References: Santos-Iglesias, P., Rosen, N. O., & Dawson, S. J. (2026). A Validation Study of the Female Sexual Function Index for Use in Pregnant and Postpartum Samples. Archives of Sexual Behavior, 55(6), 2523-2541. https://doi.org/10.1007/s10508-026-03519-w

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03519-w

Keywords: Female Sexual Function Index, sexual function, pregnancy, postpartum, psychometric validation, factor analysis, item response theory, sexual dysfunction, perinatal health, validity theory, Archives of Sexual Behavior, measurement invariance

The study, published in the journal Metabolomics, drew on 65 mother–neonate pairs recruited between August 2014 and September 2017 from the labor and delivery unit of the University of Cincinnati Medical Center. All participants delivered full-term, single infants. At delivery, the team collected 15 milliliters of maternal venous blood and 10 milliliters of venous cord blood from each pair. Cord blood is a remarkably useful proxy for the fetal circulating environment: it is drawn within minutes of birth, and because the umbilical vein carries blood to the baby from the placenta, its chemical contents reflect the fetal metabolic state just before and during the delivery process, rather than after the profound physiological transition that follows separation from the placenta.

The analytical engine of the study was untargeted high-resolution metabolomics performed by liquid chromatography coupled to a Q-Exactive HF Orbitrap mass spectrometer. Each serum sample was extracted with acetonitrile spiked with stable isotope standards, and aliquots were run in triplicate under four complementary conditions: forward-phase hydrophilic interaction chromatography in positive electrospray ionization mode, and reverse-phase C18 chromatography in negative ionization mode. The instrument operated in full-scan mode at 120,000 resolution across a mass-to-charge range of 85 to 1,275. Across all 130 samples, the team detected 17,064 molecular features — ions characterized by their mass, retention time, and abundance. After stringent filtering for reliability, including exclusion of features with coefficients of variation at or above 30 percent and missingness exceeding 20 percent, 3,641 negative-mode and 5,244 positive-mode features entered the statistical analyses.

Quality control received careful attention. Samples were randomized into four analytical batches, with maternal and neonate samples deliberately not paired within batches to prevent technical artifacts from masquerading as biological differences. Standard reference materials from the National Institute of Standards and Technology, pooled plasma quality controls, and isotopically labeled internal standards were interspersed at the beginning, middle, and end of every batch, and the ComBat algorithm was applied to remove residual batch effects. These safeguards matter enormously in untargeted metabolomics, where the sheer dimensionality of the data — thousands of features measured in dozens of samples — creates ample opportunity for false positives if analytical drift goes uncontrolled.

The first statistical test was unsupervised. Principal component analysis of the log2-transformed feature data revealed clear separation between mothers and neonates in both ionization modes. In negative mode, the first two principal components explained 15.8 percent and 12.0 percent of the variance respectively, and the two groups formed distinct clusters with 95 percent confidence ellipses that barely touched. In positive mode the separation was present but less crisp, with the first two components explaining 16.0 percent and 9.5 percent of the variance and some overlap between groups — a pattern the authors attribute in part to the high dimensionality of the data and to a small set of outliers for whom no analytical error or batch bias could be identified. Crucially, the clustering arose despite the fact that the analytical batches mixed samples from both groups, arguing that the separation reflects genuine biology rather than laboratory artifacts.

To pin down which individual features drove this separation, the team then ran a metabolome-wide association study using a linear mixed-effects model. Each feature’s intensity was modeled as the outcome, with sample type — mother or neonate — as a fixed effect and a random intercept for each mother–neonate pair, thereby exploiting the paired design to control for dyad-level factors. The models were adjusted for five pre-specified covariates: maternal age, smoking status during pregnancy, cesarean delivery, neonatal sex, and number of prior pregnancies. False discovery was controlled with the Benjamini–Hochberg procedure at a permissive 20 percent threshold, a choice the authors justify by the exploratory nature of the analysis and the modest sample size relative to the number of correlated features. The result was striking: 3,720 features — 41.9 percent of those tested — differed significantly between mothers and neonates. In negative mode, 48.5 percent of features were significant, with the majority higher in neonates; in positive mode, 37.3 percent were significant, and a remarkable 85.4 percent of those were higher in the babies.

The pathway-level analysis is where the study delivers its most interesting insight. The significant features were mapped using the mummichog algorithm against the Homo sapiens MFN pathway library, with pathway significance assessed by permutation testing across 10,000 iterations. Across all significant features, 16 unique pathways were implicated, spanning five major classes of metabolism: amino acids and nucleotides, vitamins, lipids and fatty acids, energy metabolism, and a residual category. Among the amino acid and nucleotide pathways, the enriched set included the degradation of the branched-chain amino acids valine, leucine, and isoleucine, along with tyrosine, tryptophan, lysine, beta-alanine, arginine and proline, and glycine–serine–alanine–threonine metabolism. Energy pathways including propanoate and butanoate metabolism, vitamin pathways for D3, B9, and B3, de novo fatty acid synthesis, and the urea cycle also came out significant.

The asymmetry between the two generations is the headline finding. When the team restricted the pathway analysis to features that were significantly higher in mothers alone, seven pathways emerged as enriched — prominently including three lipid-related pathways: glycerophospholipid metabolism, fatty acid activation, and de novo fatty acid biosynthesis, alongside glycosphingolipid metabolism, butanoate metabolism, and two amino acid pathways. These lipid signals are consistent with well-documented physiological adaptations of pregnancy, in which the mother ramps up glucose production and develops insulin resistance to keep a steady fuel supply flowing to the fetus, while mobilizing and synthesizing fatty acids for fetal tissue construction. But when the analysis was restricted to features higher in neonates, no pathway reached statistical significance at all. The neonatal metabolic difference, in other words, is not concentrated in any single biochemical route; it is distributed diffusely across the global metabolic profile — a pattern the authors suggest is consistent with the rapid, system-wide metabolic reprogramming the newborn must execute at birth, when the continuous placental glucose supply ends and independent energy regulation begins.

Study co-author Katherine E. Manz of the University of Michigan and colleagues frame the work as a baseline map rather than a clinical verdict. The evidence supports the conclusion that maternal metabolomes are not adequate proxies for fetal metabolomes — a finding with practical consequences. Epidemiologists studying how environmental chemicals, nutrition, and maternal health shape fetal development frequently measure metabolites in maternal blood and infer fetal exposures; this study suggests those inferences carry real uncertainty. The team’s related work in the same cohort reinforces the point: in studies of polycyclic aromatic hydrocarbons, 43 of 59 exposure-associated metabolic pathways were distinct between mothers and neonates, and endocrine-disrupting chemicals likewise produced divergent metabolic signatures in the two generations, with only partial overlap.

The authors are candid about limitations. The cohort of 65 pairs is modest, limiting statistical power, and the 20 percent false discovery threshold, while defensible for exploratory work, means some reported features may be spurious. Unmeasured confounders — maternal diet in particular, along with unknown environmental exposures — could explain some of the dyad-level variation, and because fetal blood cannot be sampled in utero, the study cannot determine when during gestation the maternal–fetal metabolic divergence first appears. What it can establish, and does, is that at the moment of birth, before any postnatal adaptation begins, mother and baby are already biochemically distinct in measurable, pathway-informative ways. As untargeted metabolomics becomes cheaper and more widespread, the practice of treating maternal blood as a window onto fetal chemistry will need recalibration — and the distinct metabolic logic of the newborn, still largely unorganized into discrete pathways at birth, represents a frontier for understanding the critical transition from fetal to independent life.

Subject of Research: Comparative untargeted metabolomics of paired maternal and umbilical cord blood samples at delivery to determine whether maternal metabolic profiles can proxy fetal metabolic profiles.

Article Title: Comparative metabolomics of paired maternal–cord samples identifies pathway-level divergence

Article References: Van Winckel, A., Gupta, M., Klein, R., Puvvula, J., Braun, J. M., Pennell, K. D., DeFranco, E. A., Ho, S.-M., Leung, Y.-K., Vuong, A. M., Percy, Z., Bhashyam, P., Lee, R., Jones, D. P., Tran, V., Kim, D. V., Huang, S., Chen, A., & Manz, K. E. (2026). Comparative metabolomics of paired maternal–cord samples identifies pathway-level divergence. Metabolomics, 22(5), Article 157. https://doi.org/10.1007/s11306-026-02528-z

Image Credits: AI Generated

DOI: 10.1007/s11306-026-02528-z

Keywords: metabolomics, pregnancy, umbilical cord blood, neonate, maternal metabolism, mass spectrometry, metabolic pathways, fetal development, lipid metabolism, principal component analysis, MWAS, perinatal health