Clinical Utility of LCT Genotyping in Children with Suspected Functional Gastrointestinal Disorder

nutrients Article Clinical Utility of LCT Genotyping in Children with Suspected Functional Gastrointestinal Disorder María L. Couce 1,2,3,* , Paula Sánchez-Pintos 1,2,3,*, Emiliano González-Vioque 1,2 and Rosaura Leis 1,3,4 1Department of Pediatrics, University Clinical Hospital of Santiago de Compostela, IDIS-Health Research Institute of Santiago de Compostela, 15704 Santiago de Compostela, Spain; [email protected] (E.G.-V.); [email protected] (R.L.) 2CIBERER, Instituto Salud Carlos III, 28029 Madrid, Spain 3Department of Pediatrics, Universidad de Santiago de Compostela, 15704 Santiago de Compostela, Spain 4CIBEROBN, Instituto Salud Carlos III, 28029 Madrid, Spain *Correspondence: [email protected] (M.L.C.); [email protected] (P.S.-P.); Tel.: +34-981950151 (M.L.C.); +34-981950134 (P.S.-P.) Received: 21 July 2020; Accepted: 28 September 2020; Published: 1 October 2020   Abstract: Genetic testing is a good predictor of lactase persistence (LP) in specific populations but its clinical utility in children is less clear. We assessed the role of lactose malabsorption in functional gastrointestinal disorders (FGID) in children and the correlation between the lactase non-persistence (LNP) genotype and phenotype, based on exhaled hydrogen and gastrointestinal symptoms, during a hydrogen breath test (HBT). We also evaluate dairy consumption in this sample. We conducted a 10-year cross-sectional study in a cohort of 493 children with suspected FGID defined by Roma IV criteria. Distribution of the C/T-13910 genotype was as follows: CC, 46.0%; TT, 14.4% (LP allele frequency, 34.1%). The phenotype frequencies of lactose malabsorption and intolerance were 36.3% and 41.5%, respectively. We observed a strong correlation between genotype and both lactose malabsorption (Cram é r’s V, 0.28) and intolerance (Cram é r’s V, 0.54). The frequency of the LNP genotype (p=0.002) and of malabsorption and intolerance increased with age (p=0.001 and 0.002, respectively). In 61% of children, evaluated dairy consumption was less than recommended. No association was observed between dairy intake and diagnosis. In conclusion, we found a significant correlation between genotype and phenotype, greater in older children, suggesting that the clinical value of genetic testing increases with age. Keywords: lactose intolerance; lactose malabsorption; C/T-13910 polymorphism; functional gastrointestinal disorders 1. Introduction The digestion of lactose, a common disaccharide in human nutrition, is dependent on intestinal lactase (LCT) activity. This enzyme, also known as lactase-phlorizin hydrolase, is a β -d-galactosidase found in the apical surface of the intestinal microvilli. LCT expression begins before birth, remains high during the nursing period, and then, in most people, progressively declines after weaning, resulting in lactase nonpersistence (LNP) [ 1 ]. This genetically programmed reduction in lactase activity varies greatly between populations, with lowest levels observed in Nordic populations (<5% in Denmark) and highest in Korean and Han Chinese populations (approaching 100%). Large variations are also observed at the regional level [ 2 ], affecting about 70% of the adult population globally. Positive selection of lactase persistence (LP) is usually explained by the gene-culture coevolution hypothesis, whereby LP confers a nutritional advantage in pastoral populations [ 3 , 4 ], and by the calcium assimilation Nutrients 2020,12, 3017; doi:10.3390/nu12103017 www.mdpi.com/journal/nutrients Nutrients 2020,12, 3017 2 of 14 hypothesis, which proposes a selective benefit of LP in environments with low ultraviolet irradiation and low dietary vitamin D intake (e.g., high-latitude regions) [ 5 ]. LP is common in people of European ancestry, especially in northwestern Europe, and declines in frequency further south and east. In Caucasians, differences in lactase activity have been linked to two single-nucleotide polymorphisms (SNPs), C/T − 13910 and G/A − 22018, both of which are located upstream of the LCT-encoding gene [ 6 ]. Both variants are in strong linkage disequilibrium, and functional evidences indicates that C/T − 13910 variant is responsible for LP. In Caucasians, the CC and GG variants are associated with hypolactasia and are good predictors of the decrease in intestinal lactase expression, whereas TT and AA - genotypes are predictors of LP. Genetic LP is considered a dominant genotype and CT and GA carriers present intermediate levels of lactase expression. LNP results from the accumulation of transcriptionally suppressive epigenetic changes in haplotypes carrying the SNP C ( − 13,010) allele, whereas in T ( − 13,910)-containing haplotypes epigenetic inactivation is avoided, facilitating LP [ 7 ]. The distribution of these different lactase phenotypes in human populations is highly variable [ 8 ]. Spanish series have reported frequencies of the LCT persistence allele ranging from 36.8% to 66% [9–12]. LP is one of the most influential factors affecting human dietary patterns [ 13 ], and it has been associated with several potential risks in adults, including a higher body mass index (BMI) in European LP populations [ 11 , 14 , 15 ], especially those that consume large amounts of dairy products [ 11 , 16 ], and metabolic syndrome [ 10 ]. Although findings are conflicting, LP is also proposed to influence bone health and fracture risk [17–19]. Several reports [ 20 – 24 ] have evaluated the clinical value of genetic testing, which can be a good predictor of LP/LNP in specific populations. A high correlation between LP and the standard hydrogen breath test (HBT) has also been demonstrated [ 21 , 24 , 25 ], even though the utility of this test may be limited by ethnicity [ 23 ]. Concordance between these two diagnostic tools appears to be influenced by age [ 26 ], with greater correlation observed in those over 30 years [ 20 ]. The pediatric clinical value of genetic testing and its correlation with HBT findings in childhood requires further evaluation, since most comparative genotyping studies have been performed in adult populations. In this study, we assessed the role of lactose malabsorption in functional gastrointestinal disorders (FGID) in children, and examined the correlation between the LNP genotype and phenotype, defined based on exhaled hydrogen (H2) measurement and gastrointestinal (GI) symptoms during HBT, in a pediatric population. 2. Materials and Methods 2.1. Study Design and Population A cross-sectional, observational, single-center study was performed at the Pediatric Gastroenterology, Hepatology and Nutrition Unit of the University Clinical Hospital of Santiago de Compostela, Spain. The study was approved by the Local Ethics Committee of Santiago-Lugo (code: 2020/319). All procedures were conducted in accordance with the Declaration of Helsinki and signed informed consent was obtained from parents and from children aged >12 years. Between 1 January 2010, and 31 December 2019, 493 consecutive pediatric patients (sample A) with GI symptoms compatible with FGID, defined according Rome IV criteria [ 27 ], were evaluated. Exclusion criteria included age >18 years and the presence of major metabolic diseases. The study population was subdivided into three age groups: ≤ 5 years; 6–11 years; and ≥ 12 years. The following variables were recorded at recruitment: age; gender; anthropometric data; family history of lactose intolerance; symptoms for which patient consulted; exhaled H2 and GI symptoms during HBT; and genetic study of SNP C/T-13910. Malabsorption was defined according to HBT test results as a H2 increase ≥ 20 ppm with respect to baseline, and intolerance was defined as the presence of symptoms during the HBT test. Nutrients 2020,12, 3017 3 of 14 In a subgroup of 120 children (sample B), we also analyzed G/A − 22018 polymorphisms and serum levels of calcium (Ca), phosphorous (P), parathormone (PTH), calcitonin, calcitriol, and 25-OH vitamin D (25-OHD), and used a validated questionnaire to collect data on dairy consumption, including the number of weekly milk, cheese, and yogurt servings and the weekly quantity of milk consumed. All biochemical measurements were obtained from fasting morning plasma samples at the same time of day (8.00 h) and all patients were free of acute infection and not receiving medication. Reference intervals for biochemical parameters are as follows: Ca: 8.6–10.3 mg/dL (ages 1–2 years), 9.2–10.3 mg/dL (ages 3–6 years), 8.7–10.5 mg/dL (males aged 6–9 years), 9.0–10.6 mg/dL (females aged 6–9 years), 9.0–10.5 mg/dL (ages 10–14 years), 8.8–10.5 mg/dL (ages 15–18 years); P: 3.1–5.6 mg/dL; PTH: 12–72 pg/mL; calcitonin: 0–18.2 pg/mL (males), 0–11.5 pg/mL (females); calcitriol: 20–54 pg/mL. 25-OHD levels are considered normal at >20 ng/mL, while levels of 12–20 ng/mL and ≤ 12 ng/mL are considered indicative of 25-OHD insufficiency and deficiency, respectively [28]. 2.2. Anthropometric Measurements Standing height was measured with a wall-mounted stadiometer and body weight, to the nearest 100 g, with digital scales. Patients were weighed barefoot after overnight fasting. BMI was calculated as weight (kg)/height 2 (m 2 ). Subjects were classified according to BMI by using WHO Child Growth Standards for underweight (BMI percentile, <5), normal weight (BMI percentile, 5–<85), overweight (BMI percentile, 85–94), and obese (BMI percentile, ≥95) [29]. Percentiles and z-scores for anthropometric measurements were calculated using the online nutritional assessment tool of the Spanish Society of Gastroenterology, Hepatology, and Nutrition (https://www.seghnp.org/nutricional/) [30]. 2.3. Analytical Measurements Concentrations of Ca and P were determined by standard procedures with the Advia 2400 Analyzer (Siemens Healthcare Diagnostic Systems, Erlangen, Germany); 25-OHD with the Advia Centaur XP Analyzer (Siemens Healthcare Diagnostics, Erlangen, Germany); PTH with Roche Cobas E 601 (Roche Diagnostic, Indianapolis, IN, USA); calcitonin with INMULITE 2000 (Siemens Healthcare Diagnostics, Erlangen, Germany); and calcitriol by radioimmunoassay in Reference Laboratory S.A. (Hospitalet de Llobregat, Spain). 2.4. Hydrogen Breath Test The HBT test consists of measurement of the concentration of H2 in exhaled air after 12 h of fasting and administration of lactose (2 g/kg up to a maximum of 50 g) diluted in aqueous solution (20%). Standard requirements for the HBT were applied: absence of treatment in the preceding 15 days with antibiotics, pre/probiotics, or laxatives, or any other drug that could alter the colonic flora; avoidance of fiber-rich food the preceding 3 days; avoidance of physical activity before or during the test; and no smoking prior to testing. A sample is obtained upon exhaling through the mouthpiece after breathing deeply. The concentration of hydrogen in the exhaled air sample is measured using a gas chromatograph (CM2 MicroLyzer, Quin Tron, Milwaukee, WI, USA). Measurements were taken immediately before (0 min, basal measurement) lactose administration and afterwards at 30-min intervals for 3 h (30, 60, 90, 120, 150, and 180 min). Peak H2was recorded. The results of the HBT were interpreted as follows: absorption (increase <10 ppm H 2 with respect to baseline); poor absorption (increase ≥ 10 ppm and <20 ppm H 2 with respect to baseline); malabsorption (increase ≥ 20 ppm H 2 with respect to baseline). Exclusion criteria included basal H 2 levels >30 ppm. If basal H 2 levels were >10 ppm and <30 ppm, we checked that the measurement at 30 min decrease. Besides, any GI symptom (abdominal pain, nausea, vomiting, diarrhea) reported by subjects during the test between 0 and 180 min were recorded. Nutrients 2020,12, 3017 4 of 14 Tolerance was defined as the absence of symptoms during the test and intolerance as the presence of symptoms during the test. 2.5. Genetic Study Genotyping of C/T − 13910 and G/A − 22018 SNPs was performed using the SEQUENOM Platform (Agena Bioscience, San Diego, CA, USA). 2.6. Statistical Analysis To verify the homogeneity of distribution of quantitative variables, we used the Student’s t-test for normally distributed variables, the non-parametric Mann–Whitney U test for variables with a non-normal distribution, and the Kruskal–Wallis test for comparisons between more than 2 groups. Normality of the variables was evaluated using the Shapiro–Wilk statistical test and homoscedasticity using the Bartlett test. In cases in which outliers were detected in the data or the conditions of homoscedasticity were not met, we employed the robust generalization of the Welch test or the Yuen test, using the trimmed mean as an estimator with a cut-offlevel of 0.2. For analysis of qualitative variables, we used the Chi-square test or Fisher’s exact test in cases of non-compliance with the assumptions of the Chi square test. The degree of association between variables was assessed using Cram é r’s V test, which returns values between 0 and 1, where 0 indicates an absence of a relationship and 1 a perfect relationship. Usually accepted cut-offpoints are as follows: 0.1, weak relationship; 0.3, median relationship; and 0.5, strong relationship. The agreement between HBT results and those of lactase genotyping was assessed using Cohen’s Kappa coefficient. Analyses were performed using R Core Team (version 3.6.3, 2020; R Foundation for Statistical Computing, Vienna, Austria). p-values obtained were adjusted using the Bonferroni correction. Only adjusted p-values <0.05 were considered statistically significant. 3. Results 3.1. Characteristics of the Study Population The study population (n=493) (sample A) showed a homogeneous distribution in terms of gender (234 males, 47.4%). Age distribution was as follows: ≤ 5 years, n=50 (10.1%); 6–11 years, n=326 (66.12%); ≥ 12 years, n=117 (23.7%). The characteristics of the study population are summarized in Table 1. The predominant symptom reported on consultation were abdominal pain, diarrhea, and nausea, and the frequency of abdominal pain (p=0.008) and nausea (p=0.009) increased significantly with age. The C/T-13910 genotype distribution was as follows: CC, 46.0%; CT, 39.5%; TT 14.4%. The frequency of the LP allele was 34.1%. A significant increase in LNP genotype was observed with increasing age (p=0.002). 3.2. Phenotype The mean frequencies of malabsorption and lactose intolerance in our series were 36.3% and 41.5%, respectively. With increasing age, the frequency of both parameters increased significantly and progressively (malabsorption, p=2.2 × 10 −16 ; lactose intolerance, p=0.003). In line with this observation, peak H2and H2increase also increased with age (p<0.001) (Table 1). As reflected in Table 2, despite the higher values obtained for peak and H 2 increase in individuals with lactose malabsorption, we found a clear difference for both markers between lactose-intolerant and lactose-tolerant children, with significantly higher values observed in the intolerant group (p<0.001). In lactose-tolerant children aged >5 years, we observed a progressive age-associated increase in time to peak H 2 (p=0.005). Time to peak H 2 was also higher in lactose-intolerant than in lactose-tolerant Nutrients 2020,12, 3017 5 of 14 children. The most frequently observed symptom induced by the HBT was abdominal pain, followed by flatulence, diarrhea, and nausea. Table 1. Characteristics of the study population. Subjects (n=493) Age Groups p ≤5 years (n=50) 6–11 years (n=326) ≥12 years (n=117) Age (mean ±SD), y 8.8 ±3.3 3.4 ±0.9 7.9 ±1.9 13.4 ±1.31 Anthropometric characteristics BMI (kg/m2)17.86 ±3.56 15.51 ±1.57 17.16 ±3.01 20.62 ±3.99 <2.2 ×10−16 BMI z-score 0.28 ±1.21 0.12 ±1.12 0.32 ±1.23 0.26 ±1.20 0.446 Underweight 64 [12.9%] 8 [16%] 40 [12.2%] 17 [14.5%] Normal weight 297 [60.2%] 32 [64%] 196 [60%] 69 [58.9%] 0.983 Overweight 64 [12.7%] 5 [10%] 44 [13.4%] 15 [12.8%] Obesity 68 [13.7%] 5 [10%] 47 [14.4%] 16 [13.6%] Family history of LI 0.996 Total 52 [10.5%] 1 [2%] 32 [9.8%] 19 [16.2%] Father 21 [4.2%] 1 [2%] 15 [4.6%] 5 [4.2%] Mother 19 [3.8%] 0 12 [3.6%] 7 [5.9%] Sibling 12 [2.4%] 0 5 [1.5%] 7 [5.9%] Previous symptoms Abdominal pain 277 [56.1%] 18 [36%] 194 [59.5%] 65 [55.5%] 0.008 Diarrhea 87 [17.6%] 11 [22%] 59 [18%] 17 [14.5%] 0.477 Nausea 34 [6.8%] 0 20 [6.1%] 14 [11.9%] 0.009 Vomiting 57 [11.5%] 2 [4%] 43 [13.1%] 12 [10.2%] 0.147 Headache 19 [3.8%] 0 13 [3.9%] 6 [5.1%] 0.313 C/T-13910 genotype CC 227 [46.04%] 12 [24%] 148 [45.3%] 67 [57.2%] 0.002 CT 195 [39.5%] 30 [60%] 127 [38.9%] 38 [32.4%] TT 71 [14.4%] 8 [16%] 51 [15.6%] 12 [10.2%] C allele frequency 65.80% 54% 64.80% 73.50% T allele frequency 34.10% 46% 35.10% 26.40% HBT findings Lactose absorption 280 [56.7%] 42 [84%] 188 [57.6%] 50 [42.7%] 1.47 ×10−5 Poor lactose absorption 34 [6.8%] 4 [8%] 21 [6.4%] 9 [7.6%] Lactose malabsorption 179 [36.3%] 4 [8%] 117 [35.8%] 58 [49.5%] Lactose tolerance 288 [58.4%] 36 [72%] 198 [60.7%] 54 [46.1%] 0.003 Lactose intolerance 205 [41.5%] 14 [28%] 128 [39.3%] 63 [53.8%] Peak H2(ppm) 36.21 ±48.1 9.56 ±14.98 35.07 ±44.1 50.79 ±61.20 <0.001 Time to peak H2(min) 88.72 ±70.61 71.4 ±75.18 87.97 ±72 98.20 ±63.34 <0.070 H2increase (ppm) 31.87 ±47.56 6.18 ±14.55 30.66 ±43.53 46.23 ±60.81 <0.001 BMI: body mass index; H 2 : hydrogen; HBT: hydrogen breath test; LI: lactose intolerance; min: minute; ppm: parts per million; SD: standard deviation; y: years. Data are expressed as the mean ± SD or as number [%]. Captions and significant values are in bold. 3.3. Phenotype/Genotype Correlation We observed moderate agreement between HBT and the results of genetic testing (Cohen’s Kappa, 0.55; 95% CI, 0.49–0.61). As shown in Table 3, C/T-13910 polymorphism was significantly correlated with phenotype of absortion/malabsorption by HBT (p<2.22 × 10 −16 ) and of lactose tolerance/intolerance (p=2.258 × 10 −09 ). Evaluation of the strength of those relationships revealed strong association for malabsorption (Cram é r’s V, 0.54), and a less strong but non-negligible association for intolerance (Cram é r’s V, 0.28). It should be noted that 98.8% of the children with lactose malabsorption were C-allele carriers, and 95.5% were homozygous for the C-allele. Nonetheless, 215 C-allele carriers were identified as lactose absorbers based on HBT results. For both CC and CT genotypes, the frequency of lactose absorbers decreased with increasing age (Table 3). Nutrients 2020,12, 3017 6 of 14 Table 2. Comparison of HBT results and induced symptoms in children with lactose malabsorption, tolerance, and intolerance, stratified according to age. Malabsorption Tolerance Intolerance p4 HBT Total ≤5 years 6–11 years ≥12 years p1Total ≤5 years 6–11 years ≥12 years p2Total ≤5 years 6–11 years ≥12 years p3 (n=179) (n=4) (n=117) (n=58) (n=288) (n=36) (n=198) (n=54) (n=205) (n=14) (n=128) (n=63) Peak H2 (ppm) 87.19 ±46.9 53.25 ±18.5 84.73 ±38.4 94.56 ±0.9 0.583 23.04 ±36.5 10.44 ±15.6 21.02 ±33.7 38.85 ±49.5 0.042 54.72 ±55.8 7.28 ±13.4 56.80 ±49.3 61.03 ±68.3 <0.001 <0.001 Time to peak H2 (Mean ±SD, min) 134.41 ± 39.7 165 ±17.3 135.38 ± 40.4 130.34 ± 38.7 0.736 78.22 ±72.8 75 ±75.04 75.90 ±7 88.88 ±62.7 0.005 103.46 ± 64.7 62.14 ±77.5 106.64 ± 62.9 106.19 ± 63.2 0.449 1.02 ×10−02 0 min 0 0 0 0 96 [33.3%] 15 [41.6%] 72 [36.3%] 9 [16.6%] 37 [18%] 8 [57.1%] 19 [14.8%] 10 [15.8%] 30 min 1 [0.5%] 0 1 [0.8%] 0 40 [13.8%] 2 [5.5%] 29 [14.6%] 9 [16.6%] 12 [5.8%] 0 9 [7%] 3 [4.7%] 60 min 16 [8.9%] 0 11 [9.4%] 5 [8.7%] 15 [5.2%] 2 [5.5%] 8 [4%] 5 [9.2%] 17 [3.4%] 0 11 [8.5%] 6 [9.5%] 90 min 27 [15%] 0 16 [13.6%] 11 [18.9%] 19 [6.5%] 2 [5.5%] 12 [6%] 5 [9.2%] 25 [12.1%] 1 [7.1%] 15 [11.7%] 9 [14.2%] 120 min 40 [22.3%] 0 24 [20.5%] 16 [27.5%] 27 [9.3%] 4 [11.1%] 10 [5%] 13 [24%] 29 [14.1%] 1 [7.1%] 20 [15.6%] 8 [12.6%] 150 min 43 [24%] 2 [50%] 29 [24.7%] 11 [18.9%] 30 [10.4%] 4 [11.1%] 22 [11.1%] 4 [7.4%] 40 [19.5%] 2 [14.2%] 25 [19.5%] 13 [20.6%] 180 min 53 [29.6%] 2 [50%] 36 [30.7%] 15 [25.9%] 61 [21.1%] 7 [19.4%] 45 [22.7%] 9 [16.6%] 45 [21.9%] 2 [14.2%] 29 [22.6%] 14 [22.2%] H2increase (Mean ±SD, ppm) 82.64 ±46.3 50.05 ±19 80.29 ±37.4 89.60 ±60.9 0.623 18.85 ±35.7 6.88 ±15.6 16.91 ±32.9 33.92 ±48.7 0.025 50.17 ±55.1 4.35 ±11.6 51.92 ±49.1 56.79 ±68.1 <0.001 <0.001 ≤10 ppm 0 0 0 0 202 [70%] 30 [83.3%] 144 [73%] 28 [51.8%] 80 [39%] 13 [92.8%] 45 [35.1%] 22 [34.9%] 10–20 pm 19 [10.6%] 0 0 0 23 [7.9%] 3 [8.3%] 14 [7%] 6 [11.1%] 13 [6.3%] 0 7 [5.4%] 5 [7.9%] 20–30 pm 14 [7.8%] 0 14 [11.9%] 5 [8.7%] 7 [2.4%] 0 6 [3%] 1 [1.8%] 8 [3.9%] 0 5 [3.9%] 2 [3.1%] 30–40 pm 10 [5.5%] 1 [25%] 10 [8.5%] 5 [8.7%] 7 [2.4%] 1 [2.7%] 4 [2%] 2 [3.7%] 7 [3.4%] 0 5 [3.9%] 3 [4.7%] 40–50 ppm 14 [7.8%] 2 [50%] 2 [1.7%] 6 [10.3%] 4 [1.3%] 1 [2.7%] 1 [0.5%] 2 [3.7%] 6 [2.9%] 1 [7.1%] 1 [0.7%] 4 [6.3%] 50–100 ppm 80 [44.6%] 1 [25%] 63 [53.8%] 29 [50%] 32 [11.1%] 1 [2.7%] 19 [9.5%] 12 [22.2%] 62 [30.2%] 0 44 [34.3%] 18 [28.5%] >100 ppm 42 [23.4%] 0 30 [25.6%] 13 [22.1%] 12 [4.1%] 0 10 [5%] 3 [5.5%] 29 [14.1%] 0 20 [15.6%] 9 [14.2%] Symptoms Abdominal pain 96 [53.6%] 1 [25%] 59 [50.4%] 36 [62%] 0.196 — — — — 165 [80.4%] 10 [71.4%] 101 [78.9%] 54 [85.7%] 0.340 Flatulence 28 [15.6%] 1 [25%] 22 [18.8%] 5 [8.7%] 0.125 — — — — 58 [28.2%] 6 [42.8%] 35 [27.3%] 17 [26.9%] 0.455 Diarrhea 36 [20.1%] 4 [100%] 28 [23.9%] 8 [13.7%] 0.173 — — — — 49 [23.9%] 3 [21.4%] 35 [27.3%] 11 [17.4%] 0.308 Nausea 16 [8.9%] 1 [25%] 10 [8.5%] 5 [8.7%] 1 — — — — 4 [13.6%] 1 [7.1%] 15 [11.7%] 12 [19%] 0.359 Vomiting 3 [16.7%] 4 [100%] 2 [1.7%] 1 [1.7%] 1 — — — — 4 [1.9%] 0 3 [2.3%] 1 [1.5%] 1 Symptoms (n) Mean ±SD 1.03 ±0.9 0.75 ±1.5 1.08 ±1.03 0.96 ±0.8 0.651 — 1.52 ±0.6 1.42 ±0.7 1.53 ±0.7 1.53 ±0.59 0.593 1 symptom 61 [34%] 1 [25%] 39 [33.3%] 22 [37.9%] — — — — 117 [57%] 10 [71.4%] 75 [58.5%] 32 [50.7%] <0.001 2 symptoms 39 [21.7%] 0 25 [21.3%] 14 [24.1%] — — — — 70 [59.8%] 2 [14.2%] 40 [31.2%] 28 [44.4%] 1.02 ×10+03 3 symptoms 14 [7.8%] 3 [75%] 11 [9.4%] 2 [3.4%] — — — — 17 [8.2%] 2 [14.2%] 12 [9.3%] 3 [4.7%] <0.001 ≥3 symptoms 1 [0.5%] 0 1 [0.8%] 0 — — — 1 [0.4%] 0 1 [0.7%] 0 HBT: hydrogen breath test; min: minutes; n: number; —: none; ppm: parts per million; SD: standard deviation. Malabsorption is defined as an expired H 2 increase ≥ 20 ppm with respect to baseline, and intolerance as the presence of symptoms during the HBT test. p 1 , comparison between age groups among children with lactose malabsorption; p 2 , comparison between age groups among children with lactose tolerance; p 3 , comparison between age groups among children with lactose intolerance; p 4 , comparison between lactose-tolerant and lactose-intolerant groups. For the malabsorption group, comparisons were made only for children ≤ 12 years and >12 years, as only 4 children ≤ 5 years presented malabsorption. Captions and significant values are in bold. Nutrients 2020,12, 3017 7 of 14 We developed a post-hoc test to examine differences in lactose absorption and lactose tolerance according to C/T-13910 genotype (Figure 1). We observed significant differences in lactose absorption between the CC genotype and the other two genotypes, and in lactose tolerance between each of the three C/T-13190 genotypes analyzed (Supplementary Table S1). Figure 1. Lactose absorption and tolerance according to C/T-13910 genotype. For the CC genotype, the frequencies of lactose malabsorption and lactose intolerance were higher than expected (positive values shown in blue). Conversely, for the CT genotype, the frequencies of lactose absorption and tolerance were higher than expected. In sample B, in which we analyzed G/A-22018 polymorphisms, all children with lactose malabsorption were G-allele carriers, and we observed significant differences in lactose malabsorption between the GG genotype and the other two genotypes analyzed. Moreover, there was a strong relationship between C/T-13190 and G/A-22018 polymorphisms (p=2.2 × 10 −16 ; Cram é r’s V value, 0.96) (Supplementary Table S1). 3.4. Dairy Product Intake and Phosphocalcic Metabolism As shown in Table 4, evaluation of 120 children using the dairy intake questionnaire revealed that 10.9% of the study population did not consume milk daily, 15.9% did not consume yoghurt daily; and 28.4% did not consume cheese daily. The majority of children, in all age groups, consumed 5–7 servings of milk per week. However, estimated milk consumption varied widely, with a mean weekly consumption of 2604.58 ± 1477.60 mL. We observed no significant differences in dairy consumption according to age (p=0.46) or polymorphism (p=0.69). The analysis of bone health biomarkers revealed no significant differences between groups with lactose malabsorption, tolerance, and intolerance or between genotypes (data not shown). Nutrients 2020,12, 3017 8 of 14 Table 3. C/T-13190 polymorphism and HBT according to age. Genotype-phenotype correlation. HBT CT-13910 Polymorphism p4 CC CT TT Total ≤5 years 6−11 years ≥12 years p1Total ≤5 years 6−11 years ≥12 years p2Total ≤5 years 6−11 years ≥12 years p3 (n=227) (n=12) (n=148) (n=67) (n=195) (n=30) (n=127) (n=38) (n=71) (n=8) (n=51) (n=12) Absorption 38 [16.7%] 7 [58.3%] 24 [16.2%] 7 [10.4%] 177 [90.7%] 28 [93.3%] 116 [91.3%] 33 [86.8%] 65 [91.6%] 7 [87.5%] 48 [94.1%] 10 [83.3%] <2.22 ×10−16 Poor absorption 18 [7.9%] 2 [16.6%] 12 [8.1%] 4 [5.9%] 0.001 11 [5.6%] 1 [3.3%] 7 [5.5%] 3 [7.8%] 0.850 5 [7%] 1 [12.5%] 2 [3.9%] 2 [16.6%] 0.412 Malabsorption 171 [75.4%] 3 [25%] 112 [75.6%] 56 [83.5%] 7 [3.7%] 1 [3.3%] 4 [3.1%] 2 [5.2%] 1 [1.4%] 0 1 [1.9%] 0 Tolerance 100 [44%] 11 [91.6%] 64 [43.4%] 25 [37.3%] 145 [74.3} 20 [66.6%] 103 [81.1%] 22 [57.8%] 43 [60.5%] 5 [62.5%] 31 [60.7%] 7 [58.3%] 1 2.26 ×10−09 Intolerance 127 [56%] 1 [8.3%] 84 [56.7%] 42 [62.6%] 0.002 50 [25.7%] 10 [33.3%] 24 [18.8%] 16 [42.1%] 0.009 28 [39.5%] 3 [37.5%] 20 [39.2%] 5 [41.6%] HBT: hydrogen breath test. Malabsorption is defined as an expired H 2 increase ≥ 20 ppm with respect to baseline, and intolerance as the presence of symptoms during the HBT test. p 1 , comparison between age groups among CC subjects. p 2 , comparison between age groups among CT subjects; p 3 , comparison between age groups among TT subjects; p 4 , correlation between phenotype by HBT and C/T-13910 genotype. Percentages are expressed in brackets. Nutrients 2020,12, 3017 9 of 14 Table 4. Dairy product consumption and phosphocalcic metabolism. Total (n=120) Age Groups p1 Tolerance (n=56) Intolerance (n=64) p2 ≤5 years (n=6) 6–11 years (n=71) ≥12 years (n=43) G/A-22018 polymorphism <2.2 ×10−16 0.002 GG 84 [70%] 1 [16.6%] 47 [66.1%] 36 [83.7%] 31 [55.3%] 53 [82.8%] GA 29 [24.1%] 4 [66.6%] 19 [26.7%] 6 [13.9%] 21 [37.5%] 8 [12.5%] AA 7 [5.8%] 1 [16.6%] 5 [7%] 1 [2.3%] 4 [7.1%] 3 [4.6%] G allele frequency [%] 82% 50% 79.50% 90.60% 74.10% 89% A allele frequency [%] 18% 50% 20.50% 9.40% 25.90% 11% Number of dairy servings/week Mean ±SD 12.89 ±4.89 11.63 ±3.14 12.87 ±5.13 12.25 ±4.71 0.46 13.35 ±4.92 11.92 ±4.80 0.165 <7 10 [8.3%] 0 6 [8.4%] 5 [11.6%] 3 [5.36%] 8 [12.5%] 7 8 [6.6%] 1 [16.6%] 5 [7%] 2 [4.6%] 4 [7.14%] 4 [6.25%] 7–10 16 [13.3%] 3 [50%] 6 [8.4%] 9 [20.9%] 7 [12.5%] 9 [14.07%] 11–14 40 [33.3%] 2 [33.3%] 25 [35.2%] 12 [27.9%] 20 [35.71%] 20 [31.26%] 14–17 31 [25.8%] 0 21 [29.5%] 9 [20.9%] 11 [19.64%] 22 [34.38%] >17 14 [11.6%] 0 8 [11.2%] 6 [13.9%] 11 [19.64%] 1 [1.56%] Number of milk servings/week 0 13 [10.8%] 0 9 [12.6%] 4 [9.4%] 0.643 5 [8.93%] 8 [12.5%] 0.501 1–2 6 [5%] 0 5 [7.2%] 1 [2.3%] 3 [5.36%] 3 [4.69%] 3–4 7 [5.8%] 0 1 [1.4%] 6 [13.9%] 3 [5.36%] 4 [6.26%] 5–7 94 [78.4%] 6 [100%] 56 [78.8%] 32 [74.4%] 45 [80.36%] 49 [76.56%] Number of yogurt servings/week 0 19 [15.8%] 1 [16.6%] 11 [15.4%] 7 [16.2%] 0.213 8 [14.29%] 11 [17.19%] 0.238 1–2 15 [12.6%] 0 8 [11.3%] 7 [16.2%] 5 [8.93%] 10 [15.63%] 3–4 30 [25%] 3 [50%] 15 [21.1%] 12 [27.9%] 15 [26.78%] 15 [23.44%] 5–7 56 [46.6%] 2 [33.3%] 37 [52.2%] 17 [39.5%] 28 [50%] 28 [43.75%] Number of cheese servings/week 0 34 [28.3%] 4 [66.6%] 18 [25.4%] 12 [28.1%] 0.872 17 [30.36%] 17 [26.56%] 0.317 1–2 37 [30.8%] 2 [33.6%] 24 [33.8%] 11 [25.5%] 12 [21.43%] 25 [39.06%] 3–4 23 [19.1%] 0 12 [16.9%] 11 [25.5%] 13 [23.22%] 10 [15.63%] 5–7 26 [21.6%] 0 17 [23.9%] 9 [20.9%] 14 [25%] 12 [18.75%] Estimated weekly milk intake Mean ±SD 2604.58 ± 1477.60 2154.16 ± 579.56 2585.71 ± 1488.12 2698.25 ± 1554.54 0.972 2771.42 ± 1545.62 2458.51 ± 1411.40 0.459 ≤500 mL 10 [8.3%] 0 7 [9.8%] 3 [6.9%] 3 [5.36%] 7 [10.93%] 500–1000 mL 10 [8.3%] 0 6 [8.4%] 4 [9.3%] 6 [10.72%] 4 [6.25%] 1000–2000 mL 23 [19.1%] 1 [16.6%] 12 [16.9%] 10 [23.2%] 9 [16.08%] 14 [21.87%] 2000–3000 mL 36 [30%] 5 [83.3%] 22 [30.9%] 9 [20.9%] 16 [25.59%] 20 [31.24%] 3000–4000 mL 21 [17.5%] 0 12 [16.9%] 9 [20.9%] 9 [16.08%] 12 [18.75%] 4000–5000 mL 11 [9.1%] 0 8 [11.2%] 3 [6.9%] 7 [12.53%] 4 [6.26%] >5000 mL 9 [7.5%] 0 4 [5.6%] 5 [11.6%] 6 [10.72%] 3 [4.68%] Phosphocalcic metabolism Calcium (mg/dL) 9.79 ±0.28 9.73 ±0.23 9.82 ±0.28 9.74 ±0.27 0.401 9.81 ±0.26 9.77 ±0.29 0.308 Phosphorous (mg/dL) 4.77 ±0.55 4.93 ±0.46 4.88 ±0.41 4.56 ±0.70 0.001 4.82 ±0.54 4.72 ±0.55 0.317 PTH (pg/mL) 40.09 ±16.64 39.83 ±21.02 37.11 ±14.03 45.04 ±21.02 0.086 38.79 ±14.38 41.23 ±18.44 0.596 25-OH vitamin D (ng/mL) 15.11 ±6.24 15.16 ±6.30 15.35 ±6.70 14.72 ±5.51 0.545 15.39 ±6.21 14.87 ±6.30 0.954 Calcitriol (pg/mL) 56.15 ±15.60 61 ±14.54 53.88 ±13.03 61 ±14.54 0.167 60.12 ±16.53 52.67 ±13.96 0.011 PTH: parathormone. Lactose tolerance is defined as the absence of symptoms during the HBT test and intolerance as the presence of symptoms during it. p 1 , comparison between age groups; p 2 , comparison of lactose tolerant versus lactose intolerant subjects. Percentages are expressed in brackets. Significant values are in bold. 4. Discussion This study of a population of children with FGID symptoms, defined according to Rome IV criteria, reveals a prevalence of lactose malabsorption similar to that of the general population, a high correlation between phenotype and genotype, and low dairy consumption. Our results show that a diagnosis of lactose malabsorption (defined based on HBT results) was established in about one third (36.3%) of children with recurrent abdominal pain due to suspected