Introduction

The Fragile X messenger ribonucleotide 1 (FMR1) gene-specific protein product, fragile X messenger ribonucleotide protein (FMRP), is critical for normal brain development based on its role in synaptogenesis, especially within the cerebral cortex, cerebellum, and hippocampus, and in modifying synaptic structure in response to environmental stimulation. Unstable mutation of FMR1 located on the long arm of the X chromosome involves the expansion of trinucleotide CGG repeats in the promotor region of the gene, with large expansions of > 200 CGG repeats considered to be “full mutation” resulting in at least partial gene methylation and deficient or absent FMRP expression. The absence of FMRP is thought to result in the clinical phenotype of full mutation, fully-methylated (FM-FM) males with FXS marked by moderate to severe intellectual disability, facial dysmorphia, anxiety, and hyperactivity. A more variable, and often milder, presentation of these clinical phenotypes is observed in females with FXS, due to random X-inactivation patterns and associated significant variation in FMR1 mRNA and FMRP expression1,2,3, and in a subset of males with FXS due to size or methylation mosaicism4.

Still, the clinical phenotype varies substantially within these subgroups (i.e., FM-FM Males vs. Mosaic Males vs. Females), which are currently defined by standard diagnostic procedures (e.g., PCR and Southern Blot). Although differences in FMR1 gene expression arising from X-linked inactivation and mosaicism account, in part, for the variable phenotypes, there are no quantitative predictors of where on the spectrum an individual with FXS will fall being used clinically. Numerous studies over the past 20 years have demonstrated that reductions in FMRP levels are associated with a greater degree of cognitive impairment among males and females with full mutation FXS5,6,7,8,9,10,11,12,13,14,15,16. Degradation in performance is observed across cognitive domains, but especially in areas of processing speed, working memory, cognitive flexibility, and inhibitory control. Despite the growing number of studies demonstrating the link between FMRP expression and IQ, to the best of our knowledge, FMRP assays have not been implemented outside the context of research to enhance care management for patients with FXS. Thus, standard of care for FXS largely continues to rely on a “one size fits all” approach. Furthermore there are several limitations of these past studies that should be noted and may contribute to the inability to translate the FMRP assay from research to clinic. For example, previous studies were largely limited to mosaic males and females. In addition, floor effects on standardized IQ measures and low sensitivity of blood sample assays to extremely low levels of FMRP has limited the ability to determine whether FMRP expression and IQ correlate among FM-FM males who tend to have no protein expression.

Our group recently optimized and validated a highly sensitive and reproducible FMRP assay that detects FMRP expression at extremely low values1. In fact, we found that 40–50% of FM-FM males, who based on standard PCR and Southern Blot testing are considered full mutation, fully methylated, express trace levels of FMRP in their blood. This is consistent with findings of incomplete silencing of the FMR1 gene reported by our group and others2,17. In each of our recent papers, we reported a small to medium-sized correlation between Deviation IQ18 and FMRP and mRNA levels1,2. However, when examining FM-FM males alone, these relationships were no longer significant. Based on visual inspection of scatterplots, it is evident that there is a wide range of Deviation IQ scores among FM-FM males in the original samples (Deviation IQ range − 20 to 47) that corresponded to no to very low protein expression. Thus, the current study aimed to examine the distribution of Deviation IQ scores in a larger sample of individual with FXS and explore the relationship with FMRP levels. Establishing whether FMRP relates to cognitive variation in FXS, especially among FM-FM males, is critical to not only our understanding the neurobiological sequelae whereby the absence or deficiency of FMRP leads to intellectual disability but also whether FMRP expression may be a useful molecular biomarker of disease severity and outcomes in FXS.

Results

Linear correlations

Across individuals with FXS, higher FMRP levels were associated with higher full-scale Deviation IQ scores (r = .73, p < .001; Fig. 1). Correlations also were significant for nonverbal (r = .68, p < .001; Supplemental Fig. 1) and verbal Deviation IQ scores (r = .68, p < .001; Supplemental Fig. 2).

Fig. 1
figure 1

Linear relationships between FMRP levels and full-scale deviation IQ in males and females with FXS. (Top) Combined sample of males with FXS (open circles) and females with FXS (open triangles) demonstrating significant relationship between higher FMRP levels and higher full-scale Deviation IQ scores. (Middle) Males with FXS (n = 99) demonstrate a significant relationship between FMRP levels and full-scale Deviation IQ scores. (Bottom) Females with FXS (n = 44) also demonstrate a significant relationship between FMRP levels and full-scale Deviation IQ scores. FMRP Fragile X Messenger Ribonucleoprotein, FXS Fragile X Syndrome, pM picomolar, IQ Intelligence Quotient, SS Standard Score.

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When examining males with FXS, FMRP levels were significantly and positively related to full-scale Deviation IQ scores (r = .22, p = .003; Fig. 1) as well as nonverbal (r = .20, p = .04; Supplemental Fig. 1) and verbal Deviation IQ scores (r = .26, p = .01; Supplemental Fig. 2). However, when restricting the male sample to FM-FM males, FMRP levels were not related to full-scale, nonverbal, or verbal Deviation IQ scores (r’s < 0.06, p’s > 0.66; Fig. 2). We also examined relationships in only males with detectable FMRP expression (i.e., FMRP > 0.07 pM). Although a trending relationship between higher FMRP and higher verbal Deviation IQ scores was noted for this subgroup (r = .24, p = .09), findings were not significant for full-scale or nonverbal Deviation IQ scores (r’s < 0.13, p’s > 0.34).

Fig. 2
figure 2

FMRP levels and deviation IQ scores do not relate in full mutation-fully methylation (FM-FM) males. In FM-FM males (n = 66), FMRP levels did not significantly related to full-scale Deviation IQ scores. Relationships also were not significant for nonverbal and verbal Deviation IQ scores (not shown). FM-FM Full mutation, fully-methylated, FMRP Fragile X Messenger Ribonucleoprotein, pM picomolar, IQ Intelligence Quotient, SS Standard Score.

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Among females with FXS, higher FMRP levels were significantly related to higher full-scale Deviation IQ scores (r = .45, p = .002; Fig. 1). Likewise, higher FMRP levels were significantly associated with nonverbal Deviation IQ (r = .46, p = .002; Supplemental Fig. 1) and verbal Deviation IQ scores (r = .34, p = .03; Supplemental Fig. 2). We statistically compared the strengths of the correlations between males and females with FXS. The differences in correlations were trending towards significantly different for relationships with full-scale (z=-1.39, p = .08) and nonverbal Deviation IQ scores (z=-1.58, p = .06), suggesting a tighter relationship between FMRP levels and Deviation IQ scores in females compared to in males. However, relationship strength did not differ between sexes for verbal Deviation IQ scores (z=-0.7, p = .26).

Normal distribution

Table 1 shows the results examining the normalcy of the distributions of full-scale, nonverbal, and verbal Deviation IQ scores for males and females with FXS. We also report findings for FM-FM and mosaic males. Briefly, we demonstrate near-normal distribution of Deviation IQ based on kurtosis, skewness, and Shapiro-Wilk test values for the full male sample as well as in FM-FM males (Fig. 3). Mosaic males had slightly higher skewness of Deviation IQ scores, indicating a leftward or negative tail (Fig. 3). Across male subgroups, verbal Deviation IQ seemed to demonstrate greater normalcy than nonverbal Deviation IQ. In females, full-scale Deviation IQ (Fig. 4) and nonverbal Deviation IQ were significant on the Shapiro-Wilk test, indicating the data was not normally distributed, consistent with higher skewness and kurtosis values. In contrast, verbal Deviation IQ appears to have a near-normal distribution in females with FXS.

Table 1 Examination of normalcy of deviation IQ scores.
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Fig. 3
figure 3

Distribution of full-scale deviation IQ scores in males with FXS with normal distribution curve overlayed. (Top) All males with FXS demonstrated a near-normal distribution of full-scale Deviation IQ scores. (Middle) Full mutation, fully methylated males showed similar near-normal distirbution of full-scale Deviation IQ scores. (Bottom) Mosaic males have more prominent skewness, but still demonstrate relative normalcy.

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Fig. 4
figure 4

Distribution of full-scale deviation IQ scores in females with FXS with normal distribution curve overlayed. Females with FXS have higher skewness and kurtosis values than males, and thus demonstrate a less normal distribution of Deviation IQ scores.

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Discussion

With our lab’s recent optimized FMRP assay that can detect levels at very low values1, it has become paramount to establish the clinical utility of this highly quantitative continuous measure. We replicate ours and others previous findings that FMRP expression and Deviation IQ are related in both males and females with FXS10,11,19. However, we also demonstrate that when examining FM-FM males alone, the relationship between FMRP and Deviation IQ is no longer significant. This is consistent with our previous study10. Notably, when examining the distribution of Deviation IQ scores in males with FXS, we found a remarkably near-normal distribution. This finding also was observed among FM-FM males with no to low FMRP expression. Hessl and colleagues20 reported that normalized cognitive scores “exhibited a more ‘normal’ distribution”, but ours is the first study that has specifically examined the skewness and kurtosis of the distribution of Deviation IQ scores in males with FXS whose FMRP expression is known. Together, these findings have several important implications. First, although our findings are largely consistent with previous findings linking FMRP expression to IQ, we show that these relationships may be more nuanced in males with FXS, such that FMRP may not have as clear of a “dose-dependent effect” on Deviation IQ as observed in females. Second, our study provides novel evidence of a “FXS IQ standard curve” that with replication in a larger sample may be useful clinically in the future. Thus, our findings suggest that FMRP deficiency drives the initial downward shift in Deviation IQ in individuals with FXS, but the remaining inter-individual variability in Deviation IQ is likely driven by combination of other heritable (e.g., parental IQ), non-heritable environmental (e.g., social determinants of health) factors in combination with other genetic and epigenetic factors known to impact IQ in typically-developing populations.

Work over the past 20 years has initially established a relationship between FMRP expression and IQ, with higher FMRP expression being associated with higher IQ scores5,6,7,8,10,11,−12,20. Because these studies were largely restricted to mosaic males and females and did not use the Deviation IQ scoring method, it has become important to determine whether significant relationships between FMRP and Deviation IQ are observed across the full spectrum of FMRP expression with the development of more sensitive FMRP assays that detect lower FMRP expression, like the Luminex assay used in the present study. Our findings largely confirm previous studies of a “dose-dependent” effect of FMRP expression on Deviation IQ in individuals with FXS. Yet, when restricting our sample to FM-FM males or only males with detectable FMRP expression, the relationships between Deviation IQ and FMRP were no longer significant.

We believe there are several reasons that may account for this finding. First, findings among FM-FM males may be partially explained by the artificial detection limits of the assays (> 0.07 pM) and the fact that ~ 50–60% of FM-FM males do not express protein. Thus, non-linear relationships may better represent these relationships in FM-FM males. Although we explored this possibility (see Methods), a larger sample may be needed. Furthermore, previous studies have reported that FMRP levels may be more strongly associated with specific cognitive domains, including processing speed and working memory. Thus, Abbreviated Deviation IQ may not have been sensitive enough to detect significant correlations in the current study5,6 and should be expanded to the full battery in future studies. Given we observed a trending relationship in males with detectable FMRP between FMRP expression and verbal (but not full-scale or nonverbal) Deviation IQ scores, this suggests that “trace” FMRP expression is still clinically meaningful, but only may be predictive of outcomes on specific cognitive domains. Last, we posit that higher levels of FMRP have a more predictable influence on Deviation IQ, whereas lower levels of FMRP have a more variable impact and could be more greatly influenced by other genetic, epigenetic, and social-environmental factors. Unfortunately, no study to date has directly tested this hypothesis. Still, findings from a recent study of individuals with FXS that found DNA methylation at birth, but not at the time of clinical testing, was associated with IQ, especially among males, help support this idea21. This previous study importantly highlights the need to examine environmental, epigenetic, and non-epigenic (i.e., cell signaling) factors that may impact cognitive outcomes in FXS as well as how the genetic, other biological, and environmental factors may have differential impact based on sex or FMRP expression.

The need to examine genetic, epigenetic, non-epigenetic and environmental factors that impact IQ is further emphasized by our current findings demonstrating a downward shifted, but otherwise normal distribution of IQ scores in FXS, especially among males. This finding has been observed in multiple other genetic syndromes associated with intellectual disability (ID) including Prader-Willi Syndrome22, Williams Syndrome23, Velo-Cardio-Facial-Syndrome24, and Tuberous Sclerosis25. Yet, it remains unknown in FXS as well as in these other syndromic IDs what accounts for the remaining ‘normal’ variance in IQ. We recently proposed a “double hit” model such that the pathogenic genetic variant contributes to the primary reduction in Deviation IQ, with secondary genetic, epigenetic, and environmental factors contributing smaller, but clinically significant, variations in individual Deviation IQ scores26. Only a few studies have explored whether these factors account for variance in IQ in syndromic IDs. For example, De Smedt and colleagues identified a positive effect of parental educational attainment level on full-scale IQ of individuals with VFC24. In FXS, Dyer-Friedman and colleagues found that both biological/genetic factors (i.e., parental IQ score) as well as social-environmental factors (i.e., parental support for learning and enrichment) accounted for variance in cognitive function in males and females with FXS13. Of note, biological/genetic and social-environmental factors affected specific aspects of cognition somewhat differently for males and females with FXS. Specifically, parental IQ broadly accounted for variance across cognitive domains in females, whereas parental IQ only impacted areas of fluid intelligence like processing speed and perceptual organization in males. In contrast, a more enriching home environment was related not only to overall cognitive development in males and females with FXS but also verbal and attention skills. These findings implicate a complex mechanism in which a combination of biological/genetic and social-environmental factors in addition to FMRP impact IQ and broader cognitive outcomes in FXS that warrant further investigation.

Clinical implications

We propose that Fragile X and these other syndromic IDs have their own Deviation IQ standard curves, each with their own mean and SD of Deviation IQ scores. Within FXS, our findings suggest there is at least a different standard curve for FM-FM males versus Mosaic males, but we even further posit that there may even be a standard curve at each FMRP level.

Furthermore, we propose one way to conceptualize these “syndromic standard curves” is to think of them as ability distributions. This can help parents and providers identify where a child with syndromic ID falls on that specific syndrome’s standard curve. In other words, where does the individual’s Deviation IQ score fall relative to other individuals with that same syndromic ID? Typically, IQ scores of individuals with syndromic ID are presented relative to typically-developing controls, which only provides minimum information of ability and impairment given the majority of syndromic IDs fall within the moderate to severe intellectual disability range. One can image the immense benefit to families and providers to be able to communicate cognitive ability in this “new” way. Providers with a caseload of a specific syndrome will be able to help caregivers more effectively discuss short- and long-term treatment planning when Deviation IQ is thought of in this lens.

For example, think about a male patient with FXS with a Full-Scale Deviation IQ of 0. Based on our findings, this score falls 1 SD below the “FXS” mean. In contrast, think about a male patient with FXS with an IQ of 45 is 1 SD above the “FXS” mean. Ability level, expectations for academic achievement and functional daily living skills, and planning for adulthood would look different for those two patients based where they fall on the “FXS” standard curve. Thus, replicating and extending findings with a much larger sample of individuals with FXS is critical to being able to put these standard curves into clinical practice.

Limitations

It is important to note our current sample overwhelmingly identifies as White, non-Hispanic, and thus our “standard curve” may be biased towards this population and not adequate represent individuals with FXS from under-represented, minority populations. In addition, we did not collect parental educational attainment from participants, furthering limiting the ability to generalize our findings. Examining these social-environmental factors as well as other biological factors contributing to inter-individual variation in Deviation IQ is outside the scope of the current study, but remains an important future direction that our group and others in the field have called for examining or re-examining26,27,28. Further, familial studies are needed to estimate the heritability of IQ in the FXS population and determine the relative contribution of biological/genetic versus social-environmental factors to variability within the distribution. Last, large-scale studies in syndromic IDs are needed to confirm whether “standard curves” exist for IQ and other clinically-relevant factors, including adaptive skills.

In conclusion, our findings replicate previous studies demonstrating the relationship between higher FMRP expression and higher Deviation IQ in individuals with FXS, yet importantly highlight the FMRP alone does not determine IQ in FXS, especially among males. In fact, we provide novel evidence that Deviation IQ is downshifted, but is otherwise relatively “normally distributed” in males with FXS, indicating FMRP deficiency is responsible for this downward shift, but does not account for the remaining inter-individual variance in IQ. There is much future work to better understand the mechanism by which deficient or absent FMRP leads to clinical phenotype and what biological/genetic, epigenetic, non-epigenetic, and/or socio-environmental factors contribute to variation in IQ scores among these males. Still our work is a critical step towards establishing molecular markers of disease severity in FXS and has critical clinical implications for treatment and care planning, especially for full mutation males with FXS.

Methods

Participant sample

Participants were recruited through the Cincinnati Fragile X Research and Treatment Center and were only included in the current study if they completed the two primary measures of interest—blood collection for FMRP processing and the Stanford Binet-5 (SB-5). A total of 144 individuals diagnosed with FXS (69% males) ages 8–50 years old completed testing (Table 2). Among the 99 males, 66 were classified as full mutation, fully-methylated, 23 as size mosaics and 8 as methylation mosaics based on standard PCR and Southern Blot testing completed by Elizabeth Berry-Kravis’s lab at Rush University. Two males had “unknown” mosaic status due to completed PCR, but not Southern Blot not yet completed to confirm methylation status.

Table 2 Demographic table.
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Blood collection, processing, and luminex-based FMRP blood quantification

Blood samples were collected in 2 mL Vacutainer K2EDTA tubes and inverted 10 times before processing to ensure homogeneity within the sample. 50 uL of blood was pipetted onto ID Bloodstain Cards (Whattman) producing two cards with 13 spots each from one sample collection. Cards were dried and stored with desiccant packs in low-gas-permeable bags to ensure DBS stability within 4–24 h after spotting. Full details describing blood processing and the Luminex-based FMRP quantification technique previously have been described1. This methodology has established test-retest reproducibility and in doing so showed the ability to consistently discriminate between zero and trace levels of FMRP in blood.

Assessment of intellectual functioning

Participants completed the Abbreviated Battery of the Stanford-Binet, Fifth Edition (SB-5). Full-scale, nonverbal, and verbal standard scores were converted to Deviation IQ scores in order to reduce floor effects present for individuals with severe cognitive impairments, and thus to better estimate intellectual ability and capture inter-individual variability18.

Statistical analysis

All statistical analyses were completed in SPSS. Linear and non-linear relationship were examined between FMRP levels and Full-Scale Deviation IQ with the entire sample and then separately for males and females with FXS. Identical analyses were performed with Nonverbal Deviation IQ and Verbal Deviation IQ scores. However, linear and non-linear models did not differ substantively across analyses, and thus only Pearson correlations between FMRP levels and IQ scores are reported in the current study. All IQ scores reported are Deviation IQ scores.

Based on our visual examination of the scatterplot reveals a wide distribution of Deviation IQ scores for FM-FM with no protein expression or very low protein expression and our previous findings of a lack of correlation between FMRP and Deviation IQ in FM-FM, we examined the normalcy of the Deviation IQ scores for males and females with FXS separately. We conducted skewness and kurtosis analyses as well as Shapiro-Wilk tests to confirm normality. In addition, in order to restrict our male sample to those with most similar genotype, we conducted similar analyses with only FM-FM.

Skewness is a measure of asymmetry, such that values of 0 indicate perfect symmetry, negative values indicate leftward skewed data (i.e., longer left tail than right), and positive values indicate rightward skewed data. In other words, a normal distribution has a skewness of 0 and near-normal distribution should have values close to 0. On the other hand, kurtosis is a measure of how “heavy” tailed the data is relative to a normal distribution. Kurtosis value of 0 indicates a normal distribution, and a positive (and high) kurtosis value reflects a “heavy tailed” distribution or data with many outliers, whereas a negative kurtosis value indicates a “light tailed” distribution that is relatively uniform or flat. By convention, skewness and kurtosis values between − 1.0 and 1.0 are acceptable, with values between − 0.5 and 0.5 indicating data are nearly symmetrical, normal distribution29.