What is the impact of stress on the development of molar incisor hypomineralization (MIH): a systematic review

Abstract

Molar Incisor Hypomineralization (MIH) is a developmental enamel defect that affects permanent first molars (PFMs) and incisors, leading to caries and early tooth loss. This systematic review investigates the literature for the relationship between stress-related factors and the occurrence of MIH. A comprehensive literature search was conducted across PubMed, Cochrane, Scopus, Google Scholar, and BASE. Articles were screened and data extracted using the Rayyan platform for systematic reviews. The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS). Six-studies were included, five examined maternal stress during pregnancy while one on stress experienced during early childhood. Out of them, five reported a significant association between stress and MIH. Given the significant variability in stress assessment methods across studies, we were able to include only two studies in the meta-analysis. Although not statistically significant, the meta-analysis showed a tendency for increased odds of MIH in the presence of stress, with an overall odds ratio of 1.46 (95% CI: 0.66–3.22). These findings suggest a potential association between psychological stress and MIH. However, further research is needed to confirm these results and elucidate the mechanisms involved. Addressing current research limitations will improve study reliability and inform preventive strategies to reduce maternal and early childhood stress, potentially mitigating MIH prevalence.

Introduction

Molar Incisor Hypomineralization (MIH) is characterized by hypomineralized, porous, and discolored enamel that affects the permanent first molars (FPMs) and incisors (Rodd et al. 2021). This condition can result in increased susceptibility to dental caries, sensitivity, and aesthetic concerns. It can adversely affect the quality of life of affected individuals by causing pain and discomfort, affecting the individual's ability to eat, speak, and engage in social activities. Additionally, the aesthetic implications of MIH can impact self-esteem and confidence (Bullio Fragelli et al. 2015; Elhennawy and Schwendicke 2016; Schneider and Silva 2018; Schwendicke et al. 2018). Treatment options for MIH vary depending on the severity of the condition and the individual's specific needs. In some cases, conservative treatments such as fluoride application and sealants may be sufficient to manage the symptoms and prevent further damage. However, more severe cases may require restorative procedures such as fillings or crowns to address the structural defects in the affected teeth. These treatments can be costly and time-consuming, adding to the burden experienced by individuals with MIH (Lygidakis et al. 2022).

MIH is a common condition with a high rate of occurrence. The reported global prevalence is approximately 13.5%, with one-third of cases being moderate to severe. In 36.6% of cases, the incisors are affected. The prevalence of MIH varies widely across different populations. The Americas reported the highest rates while the lowest rate was reported in Asia (Bukhari et al. 2023). In the Middle East, the average prevalence is around 15.05% (Bukhari et al. 2023; Sabbagh et al. 2025). Variations in prevalence could be related to variation in genetics, and environmental exposures (Lee et al. 2020).

Research suggested that genetics play a significant role in MIH. Multiple genes have been reported to be associated with MIH, including AMELX, AMBN, ENAM, and MMP-20 (Manton et al. 2020). It has been proposed that up to 20% of MIH cases may be linked to genetic factors (Bezamat et al. 2021). Although this could indicate the possible influence of hereditary components in the development of this condition, researchers are not conclusive on this relationship (Bezamat et al. 2021).

For the FPM, enamel formation process typically completes around 30–36 months (Academy and of Pediatric Dentistry. 2024). It is a slow process that occurs in multiple stages (Simmer et al. 2021). It has been suggested that environmental disturbance during this critical period may interfere with enamel development, increasing the risk of defects like MIH. Postnatal insults during this period, such as childhood illnesses—including chickenpox, asthma, tonsillitis, ear infections, and allergies, frequent antibiotic use during the first 4 years of life, and poor nutrition have been identified as potential risk factors for MIH development (Baghlaf et al. 2024; Giuca et al. 2018; Juárez-López et al. 2023; Silva et al. 2019). Additionally, prenatal, perinatal complications and problems during pregnancy have also been associated with MIH in the developing child. Multiple studies have suggested maternal illnesses, medication use during pregnancy and birth complications as significant factors (Bukhari et al. 2023; Devi et al. 2023). One study also reported that maternal vitamin D deficiency during pregnancy could increase the risk of MIH in children (Børsting et al. 2022).

Moreover, exposure to environmental toxins, such as heavy metals like lead and mercury, has been implicated in MIH development. These pollutants are believed to interfere with enamel formation, leading to structural defects (Elzein et al. 2021). It was found that Lebanese children (born in 2008, 2009, and 2010) after the 2006 war had a higher prevalence of MIH. This high prevalence has been linked to post-war environmental pollution with heavy metals (Elzein et al. 2020). Studies also suggest that urban children are at a higher risk of MIH compared to rural children, likely due to greater exposure to industrial pollutants (Głódkowska and Emerich 2020). There is also emerging evidence suggesting that physiological stress may also play a role in MIH development. Research have suggested a possible association between maternal stress during pregnancy and early childhood stress with an increased incidence of molar incisor hypomineralization (Brejawi et al. 2022; Ghanim et al. 2013).

While risk factors of MIH have been studied, the etiology of MIH is still unclear with no definite related factors. Therefore, this systematic review aims to explore the relationship between stress and MIH. Stress is a natural response to challenging situations, affecting both mental and physical health (Roberts 2020). It is linked to an increased risk of mental health issues, such as anxiety and depression (Knezevic et al. 2023; Liao et al. 2021). Studies have identified various causes of stress, including social factors like socioeconomic challenges, family dysfunction, and work-related stress, as well as environmental factors such as traumatic events, poor living conditions, cultural or societal pressures, and unpredictable life changes (Gerhardt et al. 2021; Grüning Parache et al. 2024; Reiss et al. 2019). Therefore, to understand stress in the context of its potential role in the development of MIH (Molar-Incisor Hypomineralization), this study will evaluate stress and the factors contributing to stress. The findings aim to provide valuable insights for more effective prevention and intervention strategies.

Our research question is “Does stress contribute to the development of MIH?”. The PICO of the study is: Population: children and their mothers, Exposures: stressors and mental health issues, Comparison: No psychological stress or mental health issues, and the Outcome: the presence of MIH.

A deeper understanding of the connection between psychological stress and MIH will help in developing more effective preventive strategies for MIH.

Materials and methods

The study was registered in the International prospective register of systematic reviews (PROSPERO) Registration # CRD42024616428. The approval date for the registration was 2/12/2024.

Search strategy

An inclusive literature search was performed across five major databases: PubMed, Cochrane, Scopus, Google Scholar, and BASE, January 1, 2000 to August 14, 2024. Search terms covering a combination of Medical Subject Headings (MeSH) and free-text keywords (MIH, hypomineralization, psychological factors, stress, anxiety, and risk factors). Search terms included (("MIH" OR "molar incisor hypomineralization" OR "hypomineralized molars" OR "enamel hypomineralization" OR "Molar hypomineralization") AND ("etiology" "Cause" OR"Causality" OR "Risk assessment" OR "Predisposing factors" OR "Determinants" OR "Contributing factor" OR "Correlation" OR "Association" OR "risk factor")).

Participants and eligibility criteria

Eligibility criteria were based on the PECO framework as follow:

• Population: Healthy children (Aged under 18) with no craniofacial congenital anomalies. This age was selected according to the United States National Institutes of Health, the European Union, and the United Nations and its Children’s Fund (UNICEF) (European Commission. 2024; UNICEF 2024; United States National Institutes of Health. 2024).

• Exposure: psychological stress and mental health of the mother during the perinatal period and the child during early childhood (4 years or less), as the enamel of the first permanent molar completes its formation between 30 and 36 months according to the AAPD (Academy and of Pediatric Dentistry. 2024).

• Comparison: Participants not exposed to psychological stress or systemic conditions

• Outcome: assessing MIH

• Eligible study designs included: case–control and cohort prospective or retrospective studies. Additionally, cross-sectional studies that included comparison group allowing for comparative analysis were included.

Studies that did not evaluate stress as risk factor for MIH, or included older age were excluded. Case reports, systematic reviews, animal studies and vitro studies were not included. We also excluded studies that lacked comparison group.

Data extraction

Screening and data extraction were initially conducted independently by five analysts (WHA), (YFA), (AAA), (ASA) and (EAA). Subsequently, two reviewers (WHA) and (YFA) assessed all the extracted data and discussed it collaboratively. Any disagreements were resolved by consulting an expert (HJS). We used Rayyan (https://new.rayyan.ai/), an online platform for systematic reviews to facilitate the screening process by enabling organization, tagging, and exclusion of studies based on predefined criteria. This tool streamlined the review process, particularly in managing large datasets and maintaining consistency across multiple reviewers. The following data from each included study were extracted: author names, country, date of data collection, age group, study design, sample size, stress assessment tool, reported P-values, and any adjacent factors present.

Quality assessment

Quality assessment were conducted independently by five analysts using Newcastle–Ottawa Scale (NOS). Each study received a score for criteria such as representativeness of the sample, comparability between cases and controls, and ascertainment of exposure, with each item rated as ‘Yes’ or ‘No.’ For case–control studies, scores ranged up to 10 points, with scores ≤ 3 indicating low quality, 4–6 moderate quality, and ≥ 7 high quality. Cohort studies were scored on an 8-point scale, with scores ≤ 3 as low quality, 4–5 as moderate quality, and ≥ 7 as high quality.

Results

The initial search with the above-mentioned keywords yielded 8538 potential studies. Unrelated hits and duplicates were removed leaving 136 titles for screening. Abstracts were reviewed excluding 10 studies. The full-text of the remaining 126 articles were evaluation. We excluded 89 studies for not addressing stress as an etiological factor for MIH, 30 were excluded due to unsuitable study designs, and one because of missing information. The PRISMA flow diagram summarizes this selection process, illustrating the identification, screening, and inclusion phases for studies included in the final analysis (Fig. 1). Ultimately, six studies were included in the final review.

Fig. 1.

Identification of studies via databases and registers

Characteristics of included studies

Details of included studies are presented in Table 1.

Table 1.

Characteristics of included studies

Reference	Design	Site Time	Sample size	Children examination age (years)	-Exposures risk factors assessment tool -Stress assessment tool	Exposure to stress		P-value OR/AOR (95% CI)	Adjusting Factors
						MIH (%)	Non-MIH (%)
Lee., et al. (2020)	Case–control Multi–hospitals	12-University hospitals/pediatric department. South Korea 2017	1,300 MIH = 607 Non-MIH = 584	6–13	-Maternal pregnancy exposure questionnaire -VAS maternal stress scale (1 to 10)	Mean ± SD =  4.58 ± 2.47	Mean ± SD =  4.14 ± 2.49	Model 1 OR = 1.07 (1.03–1.12)** Model 2: OR = 1.06 (1–1.12)**	Model 1 Sex and age Model 2: Sex, age, maternal stress, intake of health supplement (parental), smoking, daylight exposure and frequent cold
Brejawi., et al. (2022)	Cross-sectional retrospective study	Fujairah/United Arab Emirates 2020	162 MIH = not mentioned Non-MIH = not mentioned	7–9	-Children 6 years of life exposure -Life Events Checklist (LEC) (30 items)	Mean = 27.52*	Mean = 26.66*	P = 0.046**	No adjusting factors
(Ghanim et al. 2013)	Cross sectional study Multischool based study	52-Schools/Dental college of Mosul university, Iraq2012	823 MIH = 153 Non-MIH = 670	7–9	- Maternal pregnancy exposure questionnaire -Simple binary maternal stress yes/no question	17/153 (11.1%)	22/670 (3.3%)	AOR = 3.24 (1.33–7.88)**	Pregnancy illness, number of ultrasound, birth weight, neonatal, complications
(Mafla et al. 2024)	Cross-sectional study	School of Dentistry/Universidad Cooperativa de Colombia October 2021-March 2022	384 MIH = 128 Non-MIH = 256	6–12	- Maternal pregnancy exposure questionnaire -Perceived Stress Scale (PSS-10) Symptom of maternal depression, stress anxiety	Depression: 21/49 (42.9%) Stress: 38/128 (29.7%) Anxiety: 47/128 (36.7%)	61/335 (18.2) 40/256 (15.6%) 51/256 (19.9%)	P < 0.001** AOR = 3.26 (1.92–5.52) P < 0.001 P < 0.001 AOR = 3.49 (1.80–6.76)	House near to chemistry laboratory or factory, low SES, baby formula milk, Antibiotics in pregnancy
Kim et al. (2016)	Case–control study	Jeonju—South Korea	950 MIH = 67 Non-MIH = 883	589 were 8 361 were 9	Maternal pregnancy exposure questionnaire The Numeric Rating Scale 0—10	Mild (1–3): 26/67 (38.8%) Moderate (4–6): 17/67 (25.4%) Severe (7–10): 24/67 (35.8%)	Mild (1–3): 502/883 (56.9%) Moderate (4–6): 217/883 (24.6%) Severe (7–10): 164/883 (18.6%)	P = 0.001**	Prenatal high fever, antibiotic use Perinatal factors: low birth weight (< 2.5 kg) Postnatal factors: high fever, antibiotic use
Silva et al. (2019)	Cohort-study	2007	324 MIH = 68 Non-MIH = 256	6	Maternal pregnancy exposure questionnaire -Perceived Stress Scale (PSS-10)	Not mentioned	Not mentioned	P = 0.424 OR = 1.01 (0.98–1.05)	No adjusting factors

*calculated by the review author

**P value set at 0.05

Study design and settings

There were five case-controls (Lee et al. 2020; Brejawi et al. 2022; Ghanim et al. 2013; Kim et al. 2016; Mafla et al. 2024), and one cohort study (Silva et al. 2019). Four studies were conducted in university hospitals (Lee et al. 2020; Silva et al. 2019; Kim et al. 2016; Mafla et al. 2024). Two were community-based (Brejawi et al. 2022; Ghanim et al. 2013). The age range of examined children was generally 6 to 13 years (Lee et al. 2020; Silva et al. 2019; Brejawi et al. 2022; Ghanim et al. 2013; Kim et al. 2016; Mafla et al. 2024).

Outcomes assessed

Among the included studies, five examined maternal stress during pregnancy (Lee et al. 2020; Silva et al. 2019; Ghanim et al. 2013; Kim et al. 2016; Mafla et al. 2024), while one focused on stress experienced during early childhood (Brejawi et al. 2022). Only one study focused exclusively on stress as a factor (Brejawi et al. 2022), the other studies explored multiple potential etiological factors for MIH in addition to stress (Lee et al. 2020; Silva et al. 2019; Ghanim et al. 2013; Kim et al. 2016; Mafla et al. 2024). Of the five studies investigating maternal stress during pregnancy, four reported a significant association between stress and MIH (Lee et al. 2020; Silva et al. 2019; Ghanim et al. 2013; Kim et al. 2016). The single study that examined stress during early childhood also identified a significant association with MIH (Brejawi et al. 2022). Given the significant variability in stress assessment methods across studies, a meta-analysis was conducted where applicable. For other studies, the findings were summarized descriptively to provide an overview of the results.

Stress measurement tools

Various tools were used to measure stress levels: two study utilized Perceived Stress Scale (PSS-10) (Silva et al. 2019), with one of them also incorporating the Symptom Checklist–Revised (SCL-90-R (Mafla et al. 2024). Perceived Stress Scale (PSS-10) has 10 questions. Each question is rated on a scale from 0 to 4, where 0 means "Never," 1 means "Almost never," 2 means "Sometimes," 3 means "Fairly often," and 4 means "Very often." After answering the 10 questions, The total score is obtained by summing the responses, resulting in a range from 0 to 40, with higher scores indicating higher perceived stress levels (Cohen et al. 1983; Remor 2006). The Symptom Checklist–Revised (SCL-90-R) assesses a wide range of mental health symptoms across nine dimensions, including somatization, obsessive–compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism. It contains 90 questions, each rated on a 0 to 4 scale, where 0 means "Not at all," 1 means "A little bit," 2 means "Moderately," 3 means "Quite a bit," and 4 means "Extremely." After answering all 90 questions, the total score is calculated by summing the responses, providing subscale scores for specific symptom dimensions and an overall score reflecting the level of psychological distress. Higher scores indicate greater symptom severity and distress (Cohen et al. 1983; Remor 2006).

The Visual Analog Scale (VAS) was used by one study (Lee et al. 2020), which involved participants marking a point on a continuous line to represent the intensity of their stress levels. The scale typically ranges from 0 (no stress) to 10 (extreme stress), providing a simple and subjective measure of perceived stress. Furthermore, one study used the Life Events Checklist (LEC) to document stressful life events (Brejawi et al. 2022) which was adopted from another study that compared the stressors experienced by urban African American, White, and Hispanic children (Kilmer et al. 1998). The checklist contained 30 items, and participants answered each with "Yes" or "No. "If they answered "Yes," they rated how much the event bothered the child on a scale from 0 to 3, where 0 meant "Not at all bothered," 1 meant "A little bit," 2 meant "Somewhat," and 3 meant "A lot." The total score was calculated by summing the ratings for all items and dividing the total by 30, resulting in a possible score range of 0 to 30.

One study relied on a simple binary yes/no question regarding maternal stress during pregnancy (Ghanim et al. 2013). And another study (Kim et al. 2016) used The Numeric Rating Scale, which is a discrete numerical scale from 0 to 10 (Fillingim et al. 2016).

MIH Diagnosis criteria

Different criteria and guidelines were used to diagnose Molar Incisor Hypomineralization (MIH). These include the following:

• European Academy of Paediatric Dentistry (EAPD) Criteria: Several studies based their diagnosis of MIH on criteria outlined by the EAPD. Two studies (Lee et al. 2020; Kim et al. 2016) used the criteria established by Weerheijm et al. (Weerheijm et al. 2003). Three studies (Silva et al. 2019; Brejawi et al. 2022; Ghanim et al. 2013) in this review adopted the recent standardized approach from Ghanim et al. (2015), published in the EAPD journal (Ghanim et al. 2015), for recording MIH severity and presentation. The criteria focus on identifying enamel defects in the first permanent molars and sometimes permanent incisors. These include demarcated enamel opacities (white, yellow, or brown discoloration), post-eruptive enamel breakdown, and atypical restorations. Defects caused by trauma, fluorosis, or other conditions are excluded. Ghanim et al. (Ghanim et al. 2015) updated the EAPD criteria by introducing a detailed system for classifying MIH severity:

  1. Mild MIH: Characterized by opacities without post-eruptive breakdown, with minor aesthetic concerns or mild sensitivity.
  2. Severe MIH: Involves post-eruptive breakdown, significant sensitivity, substantial enamel loss, and major aesthetic concerns.

• Independent Diagnostic Criteria: One study (Mafla et al. 2024) did not explicitly reference international guidelines, such as those by Weerheijm et al. or the EAPD. Instead, they asked the mothers to assess their children's teeth by comparing images of MIH with their children's teeth and indicating whether they have similar lesions.

Quality assessment

Table 2 provide a structured review of the studies included in this review, using the NOS tool to evaluate study quality. Among the case–control studies (Table 2), four were rated as moderate quality and one study was rated as low quality. Most studies demonstrated inadequate ascertainment of exposure due to the subjectivity of stress measurement and recall bias. Other issues included poorly defined cases, failure to use community controls, and a lack of comparability between cases and controls.

Table 2.

Quality assessment of case–control studies using Newcastle–Ottawa Scale (NOS)

Reference	Adequate definition	Representative cases	Community controls	No history of disease for control	Comparability **	Ascertainment **	Same method of ascertainment for cases and controls	Same rate of non-response for both groups	#Yes/10
Lee., et al. (2020)	1	0	0	0	2	0	1	1	5
Brejawi., et al. (2022)	1	1	1	1	0	0	1	1	6
Ghanim et al., (2013)	1	1	1	1	0	1	1	0	6
(Mafla et al. 2024)	1	0	1	0	0	0	1	0	3
Kim et al. (2016)	1	0	1	1	0	0	1	1	5
Cohort study	Representativeness of the exposed cohort	Selection of the non-exposed cohort	Assessment of exposure	Demonstration that outcome of interest was not present at start of study	Comparability	Assessment of outcome	Was follow-up long enough for outcomes to occur?	Adequacy of follow-up of cohorts	#Yes/8
Silva et al. (2019)	1	0	0	1	1	1	1	1	6

For cohort studies, only one study, Silva et al. (2019), received a score of 6 out of 8, indicating moderate quality due to the absence of a clearly defined and comparable non-exposed cohort and inadequate ascertainment of exposure.

Meta-analysis

The studies utilized various methods and tools to assess stress and MIH. They had varying definitions, scales, number of questions and items and approaches, which made it hard to combine the results. Because of this, we were able to include only two studies, Mafla et al. (2024) and (Silva et al. 2019), in the meta-analysis. The quantitative data were analyzed using Review Manager (RevMan) Version 5.4 (The Cochrane Collaboration, 2020). The analysis was performed using a random-effects model, producing an overall odds ratio (OR) of 1.46 with a 95% confidence interval (CI) of 0.66 to 3.22. This result indicates no significant association between maternal stress and Molar-Incisor Hypomineralization (MIH). The heterogeneity across the studies was high, with an I² value of 90% and a statistically significant chi-squared test of p = 0.002, highlighting considerable variability between the included studies. Although the combined effect was not statistically significant (Z = 0.93, p = 0.35), the meta-analysis shows a tendency for increased odds of MIH in the presence of mternal stress, with an overall odds ratio of 1.46 (95% CI: 0.66–3.22). This suggests a possible association that warrants further investigation, even though the current evidence does not provide definitive proof (Fig. 2).

Fig. 2.

Meta-analysis for the relationship between MIH and maternal stress exposure during pregnancy

Discussion

Many studies have been conducted to identify the causative factors for MIH. These studies were analyzed in systematic reviews on risk factors related to early childhood illnesses, complications during delivery, and the use of antibiotics as contributing factors (Juárez-López et al. 2023; Garot et al. 2022). However, no systematic review was conducted on MIH and its relationship to maternal/infant stress exposures. Nevertheless, only few studies have investigated the effect of stress on the development of MIH. It could be due to the difficulties in measuring stress, which is mainly subjective and causes potential biases (Crosswell and Lockwood 2020). Moreover, recall bias when asking mothers if they experienced stress during pregnancy and the cultural differences in defining stressful events pose a challenge, as what is considered a stressful event in one culture may not be considered a stressful event in another culture (Chun et al. 2006; Rolan et al. 2022; Tsai and Chentsova-Dutton 2009; Yap et al. 2021).

Therefore, this systematic review was the first to tickle and critically appraise the possible role of stress in contributing to the development of MIH. The findings suggested a possible link, especially with maternal exposure to stressors during pregnancy, as five out of sex studies reported a positive relationship. However, differences in how stress was measured limited the meta-analysis to only two studies, while a descriptive summary was provided for the remaining studies.

Mechanisms of the possible relationship between stress and MIH

Stress can affect the immune system, hormone levels, and overall health. It is associated with various health issues like cardiovascular disease and gastrointestinal disorders (Kivimäki et al. 2023; Seiler et al. 2020; Tavakoli et al. 2021; Turner et al. 2020) suggesting the possibility that stress may also play a role in conditions like MIH (Brejawi et al. 2022; Mafla et al. 2024). Although the exact mechanisms linking stress to MIH remain unclear, several possible explanations have been suggested.

Enamel formation, also known as amelogenesis, is a complex process that involves the deposition of mineralized tissue by ameloblast cells. This process happens in stages and is very sensitive to outside factors (Nanci et al. 2020). Stress can disrupt amelogenesis through hormonal changes. Activation of the hypothalamic–pituitary–adrenal (HPA) axis in response to stress leads to an increase in glucocorticoid levels, which may disrupt normal enamel development (Iturriaga et al. 2024). Ameloblasts have receptors for these hormones, and when glucocorticoids bind to them, enamel formation can be disrupted (Houari et al. 2016; Jedeon et al. 2016). High cortisol levels are also linked to changes in bone metabolism, which may affect enamel mineralization (Ng and Chin 2021).

Maternal stress during pregnancy can impact the developing fetus. High levels of stress hormones like cortisol can cross the placenta, potentially affecting fetal development (Valsamakis et al. 2020). Studies have linked maternal stress to issues like preterm birth, low birth weight, and increased risk of craniofacial anomalies, autism and impaired growth (Chersich et al. 2020). Cortisol can disrupt enamel development by reducing insulin-like growth factors (IGFs), which are important for enamel production (Mountain et al. 2021; Tunheim 2022).This review found that mothers who experienced higher stress levels during pregnancy had children with a higher rate of MIH, suggesting that maternal stress and hormonal changes could disrupt normal enamel formation.

Stress can also weaken the immune system, making the body more prone to infections. Long-term exposure to high levels of cortisol can suppress the immune response, making it less effective at fighting infections. Stress-related inflammation can further weaken the immune system (Seiler et al. 2020). Research has shown that prenatal and postnatal infections or antibiotic treatments play a significant role in hypomineralization of molars and incisors, with a strong correlation to the prevalence of MIH (Juárez-López et al. 2023). Although the exact mechanism is not fully understood, it highlights the need for more research to understand how stress could contribute to MIH.

The optimal age for assessing children for MIH is around 6–8 years, as this period coincides with the eruption of the FPMs and incisors, which are commonly affected by MIH (Thorsén et al. 2022). Studies in this review generally examined children at the age ranged between 6 to 12 years, which aligns with this recommendation.

(Mafla et al. 2024) and Silva et al. 2019 assessed maternal stress during pregnancy using the Perceived Stress Scale (PSS-10), the most widely used tool for measuring perceived stress. PSS-10 is the second version of this scale. The first version is PSS-14, which contain 14 items instead of 10. The original language of the PSS is English. Mafla et al. used the validated Colombian version (Campo-Arias et al. 2009).The Perceived Stress Scale (PSS-10) capture how individuals experience and interpret stress in their lives. This subjective measurement is crucial, as health outcomes are often more closely linked to perceived stress than to mere exposure to stressors (Thorsén et al. 2022). PSS-10 is widely used all over the world and its validity and reliability have been studied extensively across diverse populations (Mozumder 2022; Simon 2021; Yılmaz Koğar and Koğar 2024).

PSS-10 is designed to measure the perception of stress over the past month, as stated in its standard instructions. (Mafla et al. 2024) had to modify the questions to fit the retrospective nature of the study, but (Silva et al. 2019) study was prospective so no modification was needed.

(Mafla et al. 2024) also used the Symptom Checklist–Revised (SCL-90-R) to measure depression, and anxiety and general mental health in addition to stress (Silva et al. 2019). While the SCL-90-R has been validated in some studies, its accuracy is debated. The tool is designed to measure nine specific types of symptoms, such as depression, anxiety, and somatization. However, many studies have found that these symptom types often overlap or do not align with the tool’s original structure. For example, instead of clearly separating depression and anxiety, the symptoms may appear mixed or grouped differently. This raises questions about whether the SCL-90-R effectively measures what it claims to Ardakani et al. (2016).

The Life Events Checklist (LEC) was used by Brejawi et al. 2022 (Brejawi et al. 2022).It measures exposure to stressful life events. It is considered an objective method to document whether a person was exposed to stressful event or not without assessing their emotional or psychological response (Gray et al. 2004). It should be noted that, exposure alone does not necessarily equate to health impact, as individuals with strong coping mechanisms may experience minimal effects (Doron et al. 2015). Unlike the PSS-10 or SCL-90-R, the LEC does not assess the emotional or psychological impact of these events. This limits its reliability in linking stress to health outcomes, as perception of stress is often more significant than the mere presence of stressors. A study published in Child and Adolescent Psychiatry and Mental Health examined the association between perceived stress and health outcomes in adolescents (Thorsén et al. 2022). The findings suggested that perceived stress was significantly associated with adverse health outcomes, highlighting the importance of subjective stress assessment.

The Numeric Rating Scale (NRS) and Visual Analog Scale (VAS), used by Kim et al. (2016) and (Lee et al. 2020) respectively. Studies have shown a good correlation between the VAS and other scales like the PSS-14 and the Hospital Anxiety and Depression Scale (HADS) (Lesage and Berjot 2011; Lesage et al. 2012, 2011). It has also been reported that the VAS correlate significantly to heart rate, blood pressure (Hulsman et al. 2010), and salivary cortisol (Hoeger Bement et al. 2010). However, we cannot ignore the fact that NRS and VAS are unidimensional scales that only measure intensity (Crisman et al. 2024). A study published in the Journal of Pain evaluated the measurement properties of the VAS and NRS in patients with low back pain. The study highlights concern about the content validity and measurement properties of the VAS and NRS, noting that these tools primarily focus on pain intensity and may not fully capture the complexity of pain experiences. Similarly, stress is a complex, multidimensional experience that involve physiological, psychological, and behavioral components (Crisman et al. 2024; Rogowska et al. 2022). The unidimensional nature of the VAS and NRS might limits their ability to fully reflect the multifaceted aspects of stress.

Ghanim et al. (2013) used a simple binary Yes/No question to assess stress. This method cannot quantify stress levels and treats all stress experiences equally, ignoring the varying health impacts of different stress intensities. Studies have shown that stress must reach a certain threshold to produce biological effects, such as releasing cortisol or increasing heart rate (Dickerson and Kemeny 2004; Widmer et al. 2005). Using a binary question will put mild and extreme stress into the same category, making it difficult to assess health outcome of stress.

None of the studies included in this review used objective biomarkers, which are considered more reliable with less potential for measurement bias (Abi-Dargham et al. 2023; Lawrence et al. 2022). Biomarkers like cortisol level, Heart rate variability, Pulse, Blood pressure, Electromyographic activity, Blood epinephrine and norepinephrine, Electrodermal activity (EDA) and Interleukin (IL)−6 provide quantifiable, reproducible stress measures that allow for comparability between studies and minimize the variability inherent in subjective tools. They assess the biological response to stressors. Various studies investigated the relationships between self-report scale and objective biomarkers for measuring stress. Weckesser et al. 2019 found no significant association between hair cortisol levels and the PSS (Perceived Stress Scale) or TICS (Trier Inventory for Chronic Stress) (Weckesser et al. 2019). Other studies, however, have found a significant link between cortisol levels and the Perceived Stress Scale (PSS) (Walvekar et al. 2015). Given the different results regarding the associations between subjective scales and biomarkers it is recommended to use both to understand the impact of stress in health outcome (Dorsey et al. 2022).

Quality of evidence and limitations

The Newcastle–Ottawa Scale (NOS) was utilized to assess the quality of the included studies and provide an indication of the level of evidence and reliability of their findings. Unfortunately, the quality of included studies ranged from low to moderate, limiting the confidence of their findings.

The main limitation was the failure of studies to use an objective method to assess exposure to stress. Different subjective scales were used, each with varying reliability, but none could provide undeniable evidence that participants were genuinely exposed to stress. Using biological markers would have offered a more definitive and objective measure of stress exposure (Dorsey et al. 2022).

Moreover, studies included in this review, except for (Silva et al. 2019), utilized a cross-sectional design. While such a design is effective in identifying associations and prevalence, it has a significant limitation in establishing causal relationship (Levin 2006; Savitz and Wellenius 2023). Other limitations include the following:

Recall bias

which can lead to inaccuracies in the data collected, as individuals may not accurately remember past events or emotions, especially in conditions like MIH, which can only be recognized after the eruption of the FPM, typically occurring around seven years after the stress exposure. A study published in BMC Pregnancy and Childbirth examined the reliability of mothers'retrospective reports of prenatal stress and distress at six months postpartum compared to data collected prospectively during each trimester. The findings indicated that while some recall was reliable, there were notable discrepancies, underscoring the challenges of relying solely on retrospective self-reports for accurate assessments (Rolan et al. 2022). Additionally, Brejawi et al. (2022), assessed early childhood stress by asking whether the children have been exposed to stressful events (Colman et al. 2016; Naicker et al. 2017). In addition to being susceptible to recall bias, the study focused solely on exposure to stress, whereas studies have found that an individual’s perception of a stressor is a more reliable predictor of health outcomes (Thorsén et al. 2022).

Sampling Bias

only Brejawi et al. (2022), Ghanim et al. (2013) and Silva et al. (2019) used samples representative of the community, recruiting participants from schools. The remaining studies selected participants from dental clinics, which represent individuals seeking treatment and may not represent the general population. This limits how much the findings can be generalized to the general population leading to Rojas-Saunero et al. (2024).

Confounding Bias

Brejawi et al. (Brejawi et al. 2022), Ghanim et al. (2013), Mafla et al. 2024 (2024), and Kim et al. (2016) did not specify whether the cases and controls were matched. Without this, it is unclear whether differences between the groups were due to stress or confounding factors such as age or underlying health conditions leading to Pearce (2016).

Cultural Bias

Brejawi et al. (2022) assessed past stressful events and family function using the Life Events Checklist (LEC), with a significant proportion of participants being Arab. Ghanim et al. (2013) assessed maternal psychological stress using a simple yes/no question combined with an interview, exclusively including participants of Arabic ethnic background. These studies may have been influenced by reporting bias, as cultural sensitivity among Arabs regarding family matters could lead to underreporting or selective disclosure of stressful events (Hammoud et al. 2005; Melhem and Chemali 2014).

A major limitation of Mafla et al. (2024) is that they relied on mothers, who are untrained, to evaluate their children’s teeth using a web-based questionnaire sent via WhatsApp. The mothers compared photos of MIH lesions to their children’s teeth and reported whether they noticed similar lesions. Although a pretest was conducted with 10 mothers assessing 10 children using provided images, the method depends on subjective judgment by non-experts for identifying MIH. Based on the European Academy of Paediatric Dentistry (EAPD) guidelines, the diagnosis of Molar-Incisor Hypomineralization should be performed by dental professionals who are trained to identify its specific clinical features (Bagher et al. 2025). Professional evaluation ensures that the diagnosis is accurate and consistent, which is critical for research validity.

Furthermore, although in the meta-analysis reported 90% heterogeneity, subgroup analysis was not feasible because the available similar data were minimal—only two studies had comparable variables that could be meaningfully grouped and analyzed (Fig. 2). As a result, the scope for performing valid subgroup comparisons was restricted.

Future research should utilize standardized tools and objective biomarkers, such as cortisol levels in saliva and hair, blood pressure, and heart rate to improve the reliability of findings. Additionally, research should examine the biological and psychological mechanisms linking stress to hypomineralization to clarify its impact on enamel formation and mineralization. Gene-environmental interaction should be also utilized and investigated (Bezamat et al. 2021). Prospective and longitudinal research with, representative samples are required to reduce reliance on long-term recall and better understand how stress influences MIH development.

Conclusions

Evidence suggests a potential link between stress and MIH. However, further research is needed to confirm these findings and clarify the underlying mechanisms. Addressing the limitations of current research will improve study reliability and aid in developing effective preventive strategies to reduce stress during pregnancy and early childhood, potentially lowering the incidence of MIH.

Author contributions

All authors made substantial contributions to the conception, methodology and design, administration, resources, software, writing of the original draft and writing and editing of the final draft of the manuscript.

Further, all authors read and approved the final draft of the manuscript.

Funding

No funding was received for conducting this study.

Data Availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interest.