Magnesium carbonate for phosphate control in patients on hemodialysis. A randomized controlled trial

Background Magnesium salts bind dietary phosphorus, but their use in renal patients is limited due to their potential for causing side effects. The aim of this study was to evaluate the efficacy and safety of magnesium carbonate (MgCO3) as a phosphate-binder in hemodialysis patients.
Introduction
Normalization of the serum phosphate level is the cornerstone of medical protocols aimed at preventing and treating secondary hyperparathyroidism. Moreover, it has been shown that elevated serum levels of phosphate and the calcium–phosphate product (Ca × P) play an important role in the development of extraosseous calcifications and are associated with increased mortality in hemodialysis patients [1, 2]. The ingestion of phosphate-binding agents in conjunction with dietary phosphate restriction and its adequate removal by dialysis are the cornerstones of serum phosphate control in end-stage renal disease (ESRD) patients. However, four decades after the introduction of chronic hemodialysis in early 1960s, we have not yet found the ideal phosphate binder(s) in terms of combined efficacy, safety and low cost. Aluminum and calcium salts and non-aluminum and non-calcium agents, such as sevelamer and lanthanum carbonate, all have advantages and disadvantages [3]. Magnesium-containing agents are aluminum- and calcium-free and are inexpensive phosphate-binders and, to date, their potential has not been explored extensively. In studies involving small series of both hemodialysis and peritoneal dialysis patients, magnesium carbonate (MgCO3) and magnesium hydroxide [Mg(OH)2] have been administered either alone or in combination with calcium salts with good results [4–10]. However, these compounds are not widely used in ESRD patients because nephrologists have an inordinate fear of hypermagnesemia and the belief that Mg administration frequently is accompanied by gastrointestinal disorders.
We carried out this study in hemodialysis patients to evaluate the efficacy and safety of MgCO3 as a phosphate-binder when given with a concurrent low dialysate magnesium solution. The control of serum phosphate in the two groups of patients was the primary outcome, while secondary outcomes were changes in serum calcium, magnesium, Ca × P and PTH levels and changes in bowel movements.
Subjects and methods
Patients and study design
Stable ESRD patients on maintenance hemodialysis in our renal unit participated in the study. The protocol of this project was approved by the Ethics Committee of the hospital. Exclusion criteria were: age <18 years, hemodialysis for less than 6 months, psychiatric or other disorders leading to non-compliance, unlikeliness to continue hemodialysis for more than 6 months in the same facility, critical illness at the time of recruitment, previous parathyroidectomy, severe hyperparathyroidism [serum intact parathyroid hormone (iPTH) > 500 pg/ml], normal serum phosphate (<5.5 mg/dl) without phosphate-binders, diseases resulting in diarrhea and the lack of informed consent. A total of 54 patients met the criteria and were approached for enrolment. The inclusion period lasted 2 months, between March 2 and April 29, 2006. Enrolled patients signed an informed consent and thereafter entered a 4-week washout period during which we withdrew all phosphate-binders as well as vitamin D medications. After the washout phase, three patients were removed because they had serum phosphate levels lower than 5.8 mg/dl; the remaining 51 patients were allocated to receive either MgCO3 tablets (MgCO3 group) or calcium carbonate tablets (CaCO3 group) for 6 months. We chose CaCO3 instead of calcium acetate because the latter is not commercially available in Greece. Four patients did not agree to consume MgCO3 but did agree to participate by taking their standard binder of CaCO3; we allocated these patients to the CaCO3 group, whereas the remainder of the patients were randomly allocated with a ratio 1:1 to either the CaCO3 (21 randomly assigned patients plus 4 = 25 patients) or to the MgCO3 (26) group. Each MgCO3 tablet contained 250 mg MgCO3, which is equivalent to 71 mg of elemental magnesium, and each CaCO3 tablet contained 420 mg of  CaCO3 equivalent to 168 mg of elemental calcium. All patients were on the same standard dialysis schedule (three times weekly × 4 h), and in all of the patients the delivered dose of hemodialysis, as calculated by applying the single pool Kt/Vurea index using the second-generation formula of Daugirdas [11],, was ≥1.35. In both groups, calcium concentration in the dialysate bath was 1.50 mmol/l, whereas magnesium concentration was 0.48 mmol/l in the CaCO3 group and 0.30 mmol/l in the MgCO3 group. The National Kidney Foundation (NKF) suggests a bath calcium dialysate concentration of 1.25 mmol/l (based on opinion, not on evidence). We used a bath concentration 1.50 mmol/l because we wanted to avoid episodes of hypocalcemia, particularly in the MgCO3 group. The patients should be more susceptible to manifest hypocalcemia since they did not receive vitamin D. The starting dose of both phosphate-binders were three tablets of MgCO3 or CaCO3 daily; the dose was adjusted thereafter according to the serum phosphate values: weekly for the first month and then monthly. The dosage of the study drug was increased to one or two tablets per meal as required to achieve the target of serum phosphate level ≤5.5 mg/dl. Based on our clinical experience, to achieve adequate control of serum phosphate we set the maximum daily dose of CaCO3 to 3780 mg (nine tablets), which is equivalent to 1512 mg of elemental calcium, and the maximum daily dose of MgCO3 to 2250 mg (nine tablets), which is equivalent to 639 mg of elemental magnesium. Each patient’s full biochemical profile was obtained at baseline, then weekly during the first month and monthly thereafter. Parathyroid hormone was measured at baseline and then at monthly intervals. If a patient developed hypercalcemia (serum Ca > 10.5 mg/dl), the daily dose of CaCO3 was reduced by one or two tablets. The same was done in patients with severe hypermagnesemia, which is defined as a serum magnesium level >3.5 mg/dl. If severe hypermagnesemia persisted for more than 3 weeks, the administration of MgCO3 was stopped, and the patient was dropped from the study. No patient received vitamin D or a calcimimetic agent during the study. Serum total calcium values were corrected to the serum albumin values.
Statistical analysis
The laboratory values were expressed as mean [± one standard deviation (SD)].
Student’s two-tailed unpaired t test was used to compare values between the two groups of patients at baseline and at 6 months. We used repeated measures of analysis of variance (repeated ANOVA method) to test for differences between the two treatment groups in average values of calcium, phosphate, Ca × P, magnesium, alkaline phosphatase (ALP) and PTH over time (time-averaged mean value differences). The chi-square test was applied to determine differences in the prevalence of laboratory data. Significance was set at a P level of <0.05.
Results
Of the 26 patients enrolled in the MgCO3 group, one dropped out due to non-compliance. Of the remaining 25 patients, two (8%) discontinued ingestion of MgCO3 and dropped out: one (4%) because of persistent diarrhea, and one (4%) because of recurrent hypermagnesemia. Of the 25 patients in the CaCO3 group; five were removed from the study (two received a kidney transplant, one died (pneumonia), one suffered a stroke and was unable to swallow tablets and one moved to another hospital). The use of phosphate-binders and vitamin D by the patients before the washout period is given in Table 1. Patients’ mean serum values at baseline and at the end of the follow-up period are shown in Tables 2 and 3. The monthly follow-up of the mean biochemical parameters are shown in Figs. 1–4. The mean Kt/Vurea was 1.379 ± 0.026 in the MgCO3 and 1.381 ± 0.027 in the CaCO3 group.
Table 1Patients’ data in terms of the use of phosphate-binders and vitamin D before the washout periodMgCO3 group (n = 25)CaCO3 group (n = 21)Vitamin D11/258/21Phosphate binders25/2521/21    CaCO31916    Sevelamer24    CaCO3 + sevelamer41Table 2Mean serum values at baselineMgCO3 groupCaCO3 groupP value (t test)Age (years)63.2365.32P = nsSD12.1911.68Calcium (mg/dl)a9.429.14P = nsSD0.540.43Phosphorous (mg/dl)6.636.58P = nsSD0.860.88Ca × P product (mg/dl)262.6260.08P = nsSD10.368.35Magnesium (mg/dl)2.382.36P = nsSD0.280.29ALP (IU/l)7686P = nsSD3735Intact PTH (pg/ml)316296P = nsSD182157SD, Standard deviation; ALP, alkaline phosphatase; PTH, parathyroid hormone; ns, not significantaSerum total calcium level corrected to serum albuminTable 3Mean values at 6 monthsMgCO3 groupCaCO3 groupP value (t test)Calcium (mg/dl)a8.979.72t = 2.16SD0.570.42P < 0.05Phosphorous (mg/dl)5.125.28t = 0.49SD0.700.74P = NSCa × P product (mg/dl)246.0451.38t = 0.30SD7.657.75P = NSMagnesium (mg/dl)2.592.40t = 1.59SD0.430.41P = NSALP (IU/l)8984t = 0.46SD2826P = NSIntact PTH (pg/ml)251212t = 0.42SD118198P = NSaSerum total calcium corrected to serum albuminFig. 1Monthly follow-up of serum phosphate (sP)Fig. 2Monthly follow-up of serum calcium (sCa)Fig. 3Monthly follow-up of serum Ca × P product (SCa × P)Fig. 4Monthly follow-up of serum PTH (siPTH)
The mean daily dose of CaCO3 was 6.76 tablets (range 3–9) containing a total of 2839 mg (range 1260–3780) of CaCO3, which is equivalent to 1136 mg of elemental calcium (range 504–1512 mg) The mean daily dose of MgCO3 was 6.21 tablets (range 3–9) containing a total of 1552 mg (range 750–2,250) of MgCO3, which is equivalent to 441 mg of elemental magnesium (range 213–639 mg). A Shapiro–Wilk test did not indicate any evidence against the normality assumption for the distribution of calcium, phosphate, Ca × P, magnesium, ALP and PTH levels within the two groups (CaCO3 and MgCO3) at any time period during the follow-up. The significance level for the normality tests was set to a = 0.001.
Average values of the biochemical data during months 1–6 are shown in Table 4. The initial mean serum phosphate levels were 6.63 mg/dl in the MgCO3 group and 6.58 mg/dl in the CaCO3 group (P = ns), while at the end of the study serum phosphate values were 5.13 and 5.26 mg/dl in the MgCO3 and CaCO3 groups respectively (P = ns; Tables 2, 3). Time-averaged mean serum phosphate values were 5.47 mg/dl in the MgCO3 group and 5.29 mg/dl in the CaCO3 group (P = ns; Table 4). Serum phosphate levels decreased by 23% in the MgCO3 group and by 19% in the CaCO3 group (P = ns). At the end of the study 17 of 23 (74%) patients in the MgCO3 and 13 of 20 (65%) of the CaCO3 group, (χ2 = 0.10, P = ns) had serum phosphate values within the range recommended by the Kidney Disease Outcomes Quality Initiative (K/DOQI) guidelines (upper threshold of 5.5 mg/dl) (Table 5).
Table 4Average serum values during months 1–6 of the follow-up periodMgCO3 groupCaCO3 groupP value (t test)Calcium (mg/dl)a9.139.60P < 0.001SD0.530.45Phosphorous (mg/dl)5.475.29P = nsSD0.810.93Ca × P product (mg/dl)250.3550.70P = nsSD7.758.07Magnesium (mg/dl)22.572.41P = nsSD0.410.32ALP (IU/l)8880P = nsSD3227Intact PTH (pg/ml)285231P < 0.01SD161177aSerum total calcium corrected to serum albuminTable 5Patients with laboratory values within the Kidney Disease Outcomes Quality Initiative (K/DOQI) guideline range at 6 monthsMgCO3 group (n, %)CaCO3 group (n, %)χ2, P valueCalcium 17/23 (73.91)5/20 (25)χ2 = 8.42, P < 0.01Phosphorous17/23 (73.91)13/20 (65)χ2 = 0.10, P = nsCa × P product20/23 (86.95)14/20 (70)χ2 = 0.99, P = nsPTH11/23 (47.82)8/20 (40)χ2 = 0.79, P = ns
Mean serum Ca values at baseline were 9.42 mg/dl in the MgCO3 group and 9.14 mg/dl in the CaCO3 group (P = ns) (Table 2). Time-averaged mean serum values were 9.13 mg/dl in the MgCO3 group and 9.60 in the CaCO3 group (P < 0.001) (Table 4). At 6 months, these values were 8.97 and 9.72 mg/dl, respectively (P < 0.05) (Table 2). Seventeen patients (17/23; 74%) who received MgCO3 and only 5/20 (25%) of those on CaCO3 had serum Ca values within the K/DQOI guidelines ( χ2 = 8.42, P < 0.01) (Table 5). We encountered six episodes of hypercalcemia, defined as a serum calcium level >10.5 mg/dl, in the CaCO3 group in comparison to one episode in the MgCO3 group.
The two groups of patients had nearly equal values of serum Ca × P product at the beginning, at the end as well as during the period study (Tables 2–4), with the MgCO3 group having 62.6 mg2/dl2 and the CaCO3 group having 60.1 mg2/dl2 (P = ns) (Table 1). Mean levels of Ca × P during months 1–6 of the follow-up were similar – 50.35 mg2/dl2 in the MgCo3 group and 50.70 mg2/dl2 in the CaCo3 group (P = ns) (Table 4); at the end of the study, the corresponding values were 46.0 and 51.4 mg2/dl2 (P = ns) (Table 3), whereas 20/23 (87%) and 14/20 (70%) patients, respectively, had Ca × P product below the upper limits of K/DQOI recommendations (P = ns) (Table 5).
Starting mean levels of serum iPTH were similar in both groups – 316 ± 182 pg/ml in the MgCO3 group and 296 ± 157 pg/ml in the CaCO3 group (P = ns) (normal range 10–65 pg/ml) (Table 2). During the 6-month follow-up period, average iPTH levels were significantly higher in the MgCO3-treated patients (285 pg/ml) than in those treated with CaCO3 (231 pg/ml) (P < 0.01) (Table 4). However, at the end of the study, those levels did not differ significantly – 251 ± 118 and 212 ± 198 pg/ml, respectively (P = ns) (Table 3). In terms of PTH levels, there was no statistically significant difference between the CaCO3 group and the MgCO3 group – 29 versus 21%, respectively  (χ2 = 0.11, P = ns). Moreover, ten of 23 (43%) patients in the MgCO3 group and nine of 20 (45%) in the CaCO3 group had initial levels of iPTH above 300 pg/ml; in three of these ten patients (30%) of the MgCO3 group and five of the nine (56%) of the CaCO3 group, the iPTH decreased below 300 pg/ml [χ2 = 1.38, P = ns; 11/23 (48%)]. Eight of 20 patients (40%) in the CaCO3 group and six of 23 patients (26%) in the MgCO3 group (P = ns) had final values of iPTH below 150 pg/ml. The number of patients of both groups, patients who received MgCO3, and eight of 20 (40%) patients who received CaCO3 who had serum phosphorus, calcium, Ca × P product and iPTH values at 6 months that fell within the range recommended by the K/DOQI guidelines are shown in Table 5 (χ2 = 0.79, P = ns).
During the follow-up period the average serum Mg levels were slightly – but not significantly – higher in the MgCO3 group than in the CaCO3 group: 2.57 vs. 2.41 mg/dl (P = ns) (Table 4). Similarly, at 6 months, these values were 2.59 and 2.40 mg/dl (P = ns) (Table 3). One patient of the 25 (4%) stopped taking MgCO3 because of recurrent high levels of serum magnesium (>3.5 mg/dl, the upper threshold according to our protocol). Two more patients manifested a transient elevation of serum magnesium – from 3.18 to 3.36 mg/dl. No patient in the CaCO3 group had a serum magnesium >3 mg/dl.
Discussion
This is the first study that compares MgCO3 with only one other phosphate-binder in hemodialysis patients. Our results show that MgCO3 administered for a period of 6 months has a good phosphate-binding ability, is well tolerated by most patients and is accompanied by a low incidence of side effects.
O’Donovan et al. [4] described 28 patients on hemodialysis who were given magnesium chloride in place of oral aluminum hydroxide. These patients were also switched from a dialysate containing 0.85 mmol/l magnesium to one not containing any magnesium at all. After 24 months of treatment on this regimen, serum phosphate was effectively controlled in these patients. The researchers saw no evidence of increased secondary hyperparathyroidism. Delmez et al. [5] conducted a 10-week, prospective, randomized crossover study of 15 hemodialysis patients, who were on MgCO3 with a dialysate magnesium concentration of 0.25 mmol/l; with this regimen, these investigators were able to reduce the CaCO3 dose and use a higher dose of calcitriol. Mean serum phosphate levels in the patients were similar with the MgCO3/CaCO3 combination as with the CaCO3 treatment alone (5.7 ± 0.2 vs. 5.2 ± 0.2 mg/dl, respectively), and there were no adverse gastrointestinal effects. Parsons et al. [6] described 32 continuous cycling peritoneal dialysis (CAPD) in patients who were dialyzed with magnesium-free dialysate and used a mixture of CaCO3 and MgCO3 as a phosphate-binder for over 1 year. These patients achieved satisfactory control of hyperparathyroidism, with normal serum calcium, phosphorous and magnesium concentrations. Other investigators have also shown that the use of magnesium-containing agents as phosphate-binders can provide effective control of serum phosphate and hyperparathyroidism and that serum magnesium levels remain within acceptable ranges [7–10]. Spiegel et al. [12] recently compared a regimen of MgCO3 and CaCO3 versus one with calcium acetate and found that the two were equally effective as phosphate-binders.
In our study, MgCO3 was well tolerated by most of our patients – only two of the 25 patients (8%) of this group were removed from the study, one for diarrhea and the other for hypermagnesemia. A third patient complained initially of mild diarrhea and abdominal discomfort, but this resolved after the first week. In comparison, five patients in the CaCO3 arm complained of constipation, but none dropped out of the study.
Serum phosphate levels after the 4-week wash-out period were 6.63 mg/dl in the MgCO3 group and 6.58 mg/dl in the CaCO3 group. These values are lower than those reported by other investigators: 7.7 mg/dl by Qunibi et al. [13] in the CARE study, 7.4–7.7 mg/dl by Chertow et al. [14] in the Treat to Goal study and 6.6–7.2 mg/dl by Asmus et al. [15]. However, in the recently presented preliminary results of the CARE 2 study [16], patients’ mean serum phosphate values were 6.5–6.6 mg/dl. Our values may have been lower because our patients adhered more closely to their diet, they were receiving an adequate dose of hemodialysis or they had neither marked hyperparathyroidism (siPTH <500 pg/ml) nor did they receive vitamin D. The Mediterranean type of diet consumed by our population on the island of Crete is less likely to cause hyperphosphatemia.
The serum phosphate level was reduced equally in both groups, and after the second month of treatment the mean values of serum phosphate in the two groups were comparable. (Table 3; Fig. 1).
In terms of the biochemical findings, the main difference between the two groups was the levels of serum calcium. Hypercalcemia is the price one has to pay for adequate phosphate-binding control with calcium-containing binders; the absence of hypercalcemia is the major advantage of magnesium-based phosphate-binders.
We observed that patients receiving MgCO3 were more likely to have serum calcium levels within the K/DQOI guidelines [17] than those on CaCO3. Both groups of patients achieved a mean serum Ca × P product of <55 mg2/dl2 (Tables 3, 4). Consequently, a significant proportion of patients had a Ca × P product within the K/DQOI recommendations (Table 5).
The risk of severe hypermagnesemia is a major concern when magnesium salts are administered to hemodialysis patients. In such patients serum magnesium levels depend chiefly on the dialysate concentration of this ion, which standardly ranges between 0.45 and 0.50 mmol/l. In the MgCO3 group we used a low magnesium dialysate, 0.30 mmol/l, and this enabled us to avoid extreme hypermagnesemia and keep serum magnesium levels within acceptable ranges.
Clinical symptoms and ECG disorders due to hypermagnesemia appear only when the level of serum magnesium >4 mg/dl [18]. The ionized fraction that represents approximately 60% of the total serum magnesium is the biologically active form of this element. Truttmann et al. [19] and Saha et al. [20] recently demonstrated that the ionized fraction of magnesium lower in HD patients than in individuals with normal renal function; therefore, the incidence of ‘real hypermagnesemia’ may be overestimated in these patients. Of note is that serum magnesium accounts for approximately 1% of the total body magnesium since it is mainly an intracellular cation. Estimation of total body magnesium is based on the determination of intracellular magnesium (IcMg) levels in skeletal muscles or in peripheral lymphocytes [21]. Calculations on the levels of magnesium in these tissues in uremic patients have produced conflicting results, with positive or neutral or even a negative correlation with serum magnesium being found [22, 23].
Although we did not examine the consequences of hypomagnesemia and high serum magnesium levels, various studies have shown that hypomagnesemia plays a significant role in the pathogenesis of cardiovascular diseases and that a high serum magnesium level may retard the development and/or acceleration of arterial atherosclerosis [24, 25]. Meema et al. [26] first showed that ‘hypermagnesemia might be associated with retardation or improvement of arterial calcifications in peritoneal dialysis patients’. Izawa et al. [27] described a hemodialysis patient in whom soft-tissue calcifications resolved after treatment with a dialysate with a  high concentration of magnesium. Two studies by our team [28, 29] clearly demonstrated that the absence of mitral annular calcifications and a thinner carotid intima media thickness in hemodialysis patients both correlated with higher levels of serum and intracellular magnesium. In a previous study, we showed that both intracellular and serum magnesium concentrations were directly associated with 5-year survival in 94 hemodialysis patients of our unit [30].
One of the secondary objectives of the study was to investigate if MgCO3 effectively controls serum iPTH. Calcium carbonate was more effective than MgCO3 in decreasing serum iPTH. In the MgCO3-treated patients time-average mean values of iPTH during months 1–6 were significantly higher than in those treated with CaCO3 (Table 4). However, the final levels of serum iPTH at 6 months did not differ significantly between the two groups. Furthermore, at the end of 6 months, a slightly higher number of patients (48%) in the MgCO3 group than in the CaCO3 (40%) group achieved serum iPTH values in the range of 150–300 pg/ml.
In the presence of almost equal serum P values in the two groups, we hypothesize that the relatively higher PTH reduction in the CaCO3 group was due to the significant higher concentration of serum calcium in this group. On the other hand, high levels of serum magnesium have a similar, but weaker, action in suppressing PTH secretion [31]. Figure 4, which shows the monthly follow-up of serum iPTH levels, shows that the curve of the change in mean iPTH values is different in the MgCO3-treated patients in that the mean values increase during the first month of the treatment, subsequently falling (but slower than in the CaCO3 group) and finally, in the sixth month, they approach the mean iPTH values of the CaCO3 group. We speculate that the shape of this curve indicates that suppressive action of serum magnesium on the parathyroid glands is weaker but delayed compared to that of serum calcium. The number of patient who had final serum iPTH values of <150 pg/ml, which could be associated with adynamic bone disease, did not differ in the two groups. However, the impact of the two regimens on the patients’ bone turn over was not included in the objectives of the study, so we did not obtain data associated with bone metabolism markers.
Our experience suggests that a significant proportion of hemodialysis patients need more than one agent to achieve a satisfactory phosphate-binding. In such cases, MgCO3 could be if not the primary at least the second constituent of the phosphate-binding regimen, combined ideally with a calcium-containing salt. An additional advantage is that MgCO3 is much less expensive than the newest sevelamer HCL and lanthanum carbonate.
The limitations of our study are: (1) it is a single-center study, although this has also a number of advantages; (2) the number of the participant patients was not large, although it is one of the largest groups reported to date; (3) the allocation of the patients to the two regimens was only partially random; (4) the relatively low starting serum phosphate level in conjunction with the no vitamin D use means that the patients were not typical of most HD patients; (5) we did not obtain data on the patients’ bone metabolism markers.
In conclusion, our study showed that MgCO3 administered for a period of 6 months is an effective and inexpensive agent to control serum P levels in hemodialysis patients. Gastrointestinal disorders due to its use were minor, while its administration in combination with a low dialysate magnesium concentration reduces the risk of severe hypermagnesemia. Patients treated with MgCO3 had a mild suppression of PTH, an optimum regulation of Ca × P product, relatively low serum calcium and no episodes of hypercalcemia. We believe that this demonstration of the effectiveness of MgCO3 to bind phosphate warrants further investigation in a larger group of patients.

magnesium carbonate
hemodialysis
phosphate-binders
end-stage renal disease


Heritability of Self-reported Phobic Fear

Twin studies on fear and phobia suggest moderate genetic effects. However, results are inconclusive regarding the presence of dominant genetic effects and sex differences. Using an extended twin design, including male and female twins (n = 5,465) and their siblings (n = 1,624), we examined the genetic and environmental influences on blood-injury, social, and agoraphobic fear and investigated their interaction with sex and age. Data of spouses (n = 708) of twins were used to evaluate assortative mating for the three fear dimensions. Results showed that there was no assortative mating for blood-injury, social and agoraphobic fear. Resemblance between biological relatives could be explained by additive and non-additive genetic effects for blood-injury and agoraphobic fear in all participants, and social fear in participants aged 14–25 years. For social fear in participants aged 26–65 only additive genetic effects were detected. Broad-sense heritability estimates ranged from 36 to 51% and were similar for men and women.
Introduction
Fears and phobias are extremely disruptive and distressing disorders that have a great impact on an individual’s life (Mendlowicz and Stein 2000; Saarni et al. 2007). The prevalence of anxiety disorders is high and anxiety disorders represent a serious public health concern (Kessler et al. 2005). However, the etiology of phobic fear is still poorly understood.
Family studies of clinically defined phobias have consistently found evidence for the familial aggregation (Fyer et al. 1995; Stein et al. 2001). Twin family studies of clinically defined fears and phobias have also indicated familiarity (Torgersen 1983; Skre et al. 1993; Skre et al. 2000). Due to their relatively small sample sizes, clinical studies often are unable to establish whether this familiarity results from genetic or environmental similarities. Through community based twin and twin family studies, large samples can be assessed, resulting in sufficient power to disentangle genetic and environmental influences in the etiology of fears and phobias. Such studies have suggested an important role for genetic and unique environmental factors (e.g., aversive experiences) in the origin of fears and phobias, while shared environmental influences appeared to be of little etiological significance (Hettema et al. 2001; Sundet et al. 2003).
The Virginia Twin Registry investigated the familial aggregation of phobia diagnoses in a large adult sample. In women, heritability estimates of 39% for agoraphobia and 30% for social phobia were found (Kendler et al. 1992). In men, these estimates were 37 and 20% (Kendler et al. 2001). Familial aggregation of blood-, needles-, hospital-, or illness-phobia was studied in female twins only and appeared to be due to shared environmental factors, although the statistical power to differentiate between additive genetic and common environmental influences was low (Neale et al. 1994a). An Australian study of self-reported fear of blood-injury-injection in female twins also found significant familial aggregation of blood-injury-injection fears. It was not possible to differentiate between genetic and shared environmental influences as the explanation for familial aggregation. Multivariate genetic analysis of fainting and blood-injury-injection fear with and without fainting indicated that the genetic variance in blood-injury-injection fear was attributable to additive genetic factors shared with fainting (55%) (Page and Martin 1998).
Based on the correlation structure several studies suggested the presence of non-additive genetic effects in phobic fear, but due to insufficient statistical power, these effects could not convincingly be demonstrated (Skre et al. 2000; Neale et al. 1994a; Rose and Ditto 1983; Kendler et al. 1999; Page and Martin 1998).
The heritability estimates reported in the Kendler et al. studies of female (1992) and male twins (2001) seem to indicate lower heritability estimates for fear in men than in women. Together with the fact that women display generally more phobias than men (Fredrikson et al. 1996; Furmark 2002; Bekker 1996; Curtis et al. 1998; Bijl et al. 2002; Middeldorp et al. 2005) this suggests that men and women differ in the extent to which genetic and environmental risk factors affect phobic fear. Sex differences in the genetic contribution to the etiology of phobic fear have only been formally tested in the Virginia Twin Registry, in the Netherlands Twin Register and in the Australian Twin Registry. In the Virginia Twin Registry, Kendler et al. (2002) found equal heritability in men and women for agoraphobia and blood-injury phobia. For social phobia, on the other hand, resemblance in men was explained by genetic factors while in women resemblance was explained by shared environment. Because earlier studies relying on a female sample (Kendler et al. 1992, 1999) indicated that familial resemblance was entirely due to genetic factors, the authors attributed this sex difference to stochastic factors. However, results from two multivariate studies using data from the Virginia Twin Registry (Hettema et al. 2005, 2006), indicated that around 10% of the variance in social phobia was explained by shared environmental factors.
Qualitative sex differences on the liability for agoraphobia, social phobia and blood-injury phobia were suggested by Kendler et al. (2002) based on the dizygotic opposite sex (DOS) twin correlation, which was lower than the dizygotic (DZ) same sex correlations. A second study of the Virginia Twin Registry (Hettema et al. 2005) found no sex differences in the genetic and environmental risk factors for agoraphobia or social phobia. In a combined Dutch and Australian sample, Middeldorp et al. (2005) failed to find quantitative sex differences in the familial influences on agoraphobia and social phobia. For agoraphobia a low correlation in DOS twins suggested qualitative sex differences. To summarize, most studies report no sex differences in the heritability of fear and phobia while some do suggest qualitative sex differences based on the DOS and DZ correlation structure.
Spousal resemblance is often found for psychiatric disorders and related phenotypic traits. The most cited explanation for spousal similarity is assortative mating, meaning that spouses are more similar for a trait or disorder than expected under random mating (Merikangas 1982; Garrison et al. 1968), although significant spousal correlation could also indicate, for example, marital interaction or social stratification. Spousal resemblance is an important issue to consider because increased phenotypic correlation between spouses increases genetic variance in the offspring generation. When non-random mating for a heritable trait is present but this is not specifically included in the twin model, heritability estimates may be downwardly biased. For depressive disorders, a meta-analysis reported marital resemblance for depression in 12 of 17 studies (Mathews and Reus 2001). An extensive study on spousal similarity for psychiatric disorders in a population-based sample was carried out by Maes et al. (1998). Several psychiatric diagnoses were examined, including generalized anxiety disorder, major depressive disorder, panic disorder and phobias. Moderate spousal correlations were seen for most psychiatric diagnoses, but for phobias spousal correlations were not significant.
So far, age effects on the heritability of phobic fears have not received much attention in the literature. This is an omission because it has well been established that some specific fears decline from adolescence to later life (e.g., fear of blood, strangers or personal death) (Bienvenu and Eaton 1998; Hall 1897; Rose and Ditto 1983; Kessler et al. 1994; Henderson et al. 1998), while others increase or are not affected by age at all (e.g., fear of negative social interactions or small organisms) (Rose and Ditto 1983; Lapouse and Monk 1959). Rose and Ditto (1983) found evidence that with age there is a change in the genetic and environmental influences on fear of death. However, a more recent study reported that there were hardly any longitudinal changes in the genetic and environmental determinants of anxiety (Gillespie et al. 2004).
In order to replicate and extend previous research on fears and phobias, we examined the heritability of three classes of phobic fears (i.e., blood-injury, social, and agoraphobic fear) using longitudinal data from Dutch twins and their siblings registered with the Netherlands Twin Register (Boomsma et al. 2006). The inclusion of siblings and repeated observations increases statistical power to discriminate between additive genetic and non-additive genetic effects and to detect sex specific genetic effects. Adding siblings to the sample also provides the opportunity to examine twin-specific effects on phobic fears (Posthuma and Boomsma 1999, 2000). Data were also available for a subsample of spouses of twins which were used to estimate the correlation between spouses to examine whether there is evidence for assortative mating. Since the determinants of phobic fear in adolescence and young adulthood may differ from those in later life, we divided the dataset into two age cohorts so as to test for differences in heritability between the two cohorts. In addition, we tested for qualitative and quantitative sex differences in the etiology of phobic fear.
To summarize, in this report, we address the following questions. First, is there spousal similarity for blood-injury, social and agoraphobic fear? Second, what are the heritability estimates for these three subtypes of fear? Third, are there sex and age differences in the heritability estimates of these subtypes?
Methods
Participants
This study is part of an ongoing study on health and lifestyle in twin families registered with the Netherlands Twin Register (Boomsma et al. 2006). Initially, adolescent and adult twins and their family members were recruited through City Councils in 1990–91 and in 1992–93. After 1993, an effort was made to recruit older twins through a variety of approaches. Siblings and spouses of twins were recruited in the study since 1995 and 2000, respectively. Parents did not participate in 1997 and 2000. All registered individuals gave their informed consent before they filled out questionnaires. Further details on response rates and demographic characteristics of the sample can be found elsewhere (Boomsma et al. 2002; Vink et al. 2004; Boomsma et al. 2006). The data on phobic fear were derived from surveys sent in 1997 and 2000. Table 1 shows the complete sample configuration of two age cohorts in 1997 and 2000. In total, there were 7,089 subjects (5,465 twins and 1,624 siblings) from 3,471 families, with longitudinal data available for 2,537 individuals. Based on the age of the twins, we subdivided the participating families into two age groups: a 14–25 year group (n = 4,275 individuals) and a 26–65 year group (n = 2,814 individuals). Families with siblings but without twins were assigned to one of the two age groups according to the age of the youngest sibling. There was a small overlap between the two age cohorts in age of the non-twin siblings. In the 14–25 year age group, there were 1,987 families of which 1,004 included one or more family members who participated at both time points. The 26–65 year age group involved 1,484 families of which 435 contained one or more family members who participated at both time points. In the youngest age group, mean age was 21.2 years (SD = 3.5) in 1997 and 23.2 years (SD = 3.6) in 2000. In the oldest age group, mean age was 36.6 years (SD = 10.5) in 1997 and 38.0 years (SD = 10.4) in 2000. For the 2000 survey, we asked spouses of all twins aged 25–30 years to participate. In total 708 spouses completed a survey.
Table 1Family configuration in the sample according to zygosity, cohort and number of additional non-twin siblings of the two time points (separated by the slash; 1997/2000)Zero siblingsBrotherSisterBrother and SisterTotalAge 14–25MZMTwin pair70/10441/2935/279/4155/164Single twin21/574/610/111/336/77DZMTwin pair44/4533/1526/143/7106/81Single twin18/444/910/101/233/65MZFTwin pair145/23049/3562/5612/9268/330Single twin49/10610/1211/173/273/137DZFTwin pair84/10834/1937/277/7162/161Single twin33/8710/912/232/257/121DOSTwin pair101/10564/3768/4110/3243/186Single twin69/18311/2625/326/2111/243No twins23/4338/722/463/119Total age 14–25634/1069283/240334/33056/451307/1684Age 26–65MZMTwin pair16/608/67/115/836/85Single twin20/686/34/76/136/79DZMTwin pair11/313/63/77/324/47Single twin20/544/27/55/536/66MZFTwin pair64/22314/2223/539/12110/310Single twin30/1226/1010/162/448/152DZFTwin pair29/958/1411/153/651/130Single twin39/994/914/73/060/115DOSTwin pair27/869/710/154/750/115Single twin36/1206/810/226/558/155No twins21/4446/2512/879/77Total age 26–65292/95889/131145/18362/59588/1331Total number of families926/2027372/371479/513118/1041895/3015MZM = monozygotic males; DZM = dizygotic males; MZF = monozygotic females; DZF = dizygotic females; DOS = dizygotic opposite sex twins
Zygosity
A total of 1,322 twins were classified as DZ because they were of opposite sex. Zygosity of same sex twins was determined from DNA polymorphisms (n = 1,205), or from survey questions about physical twin resemblance and confusion of the twins (n = 2,938). Based on the answers from all available surveys, zygosity was determined. When there was an inconsistency over time or persons, the majority of the judgements determined the final outcome. The agreement between zygosity based on survey data and on DNA typing was 97% (Willemsen et al. 2005).
Measures
Participants completed the Dutch version of the Fear Questionnaire (FQ; Marks and Mathews 1979). The FQ is a widely used instrument that possesses good psychometric qualities (Van Zuuren 1988). The FQ contains 15 items that tap phobic avoidance in three domains: blood-injury fear (e.g., avoidance of hospitals), social fear (e.g., avoiding talking to people in authority), and agoraphobic fear (e.g., avoiding large, open spaces). Each domain is represented with five items. Subjects were instructed to indicate on a nine point scale (0 ‘would not avoid it’ to 8 ‘always avoid it’) how much they would avoid certain situations because of fear or other unpleasant feelings. Scores were summed across subscales to obtain scores for blood-injury fear, social fear, and agoraphobic fear. A score was only calculated if at least four out of five items of a subscale were answered. Missing answers and double entries were substituted by the mean item score. We checked the factor structure of the FQ in our sample. All items of the FQ were analyzed with PCA with promax rotation in SPSS. Analyses were done separately for the two time points with random selection of one person per family. At both time points we found the original three factor solution in our data which accounted for around 47% of the variance.
Twin-family Studies
Twin studies make use of the genetic relatedness of twins and their family members to address questions about the etiology of population variation. Monozygotic (MZ) twins are genetically (nearly) identical. Dizygotic (DZ) twins and siblings share on average 50% of their segregating genes. Additive genetic effects (A) are suggested if the correlation for a phenotype in MZ twins is larger than the correlation in DZ twins and siblings. When the DZ correlation is more than half the MZ correlation, this indicates environmental effects shared by members from the same family (C). When the DZ correlation is less than half the MZ correlation, this can be taken as evidence for non-additive genetic effects (D). Non-additive genetic effects can consist of interactions between alleles within a locus (dominance) or across different loci (epistasis). Differences in fear scores within MZ twin pairs are due to unique environmental influences (E), which also include measurement error. The observed variance in phobic fear can thus be decomposed in four possible sources of variance; A, C, D, and E. However, the observed variances and covariances only provide enough information to test either an ACE model or an ADE model. Based on the pattern of twin correlations (see results section), A, D, and E were modelled in this study.
Statistical Analysis
Since phobic fear is not normally distributed in the population we used a multiple threshold model (see Fig. 1) to estimate the genetic and environmental contributions to the liability of phobic fear. Liability to phobic fear is assumed to be continuous and normally distributed in the population. Derks et al. (2004) showed that this is the optimal approach when analyzing L-shaped distributed phenotypic data. We divided the liability to phobic fear in three categories separated by two thresholds so that all categories contained approximately equal numbers of subjects (see Fig. 2). The validity of the bivariate threshold model to the fear data divided in three categories, was assessed in PRELIS (Jöreskog and Sörbom 1996). The fit of the bivariate threshold model to the blood-injury, social and agoraphobic fear data was good; almost all RMSEA’s were <0.05 and all P values exceeded the 0.002 significance level (Bonferroni correction for multiple testing).
Fig. 1Threshold model of the liability for phobic fear. Latent factors are symbolized in circles, observed phenotype as squares. E, unique environmental factor; D, non-additive genetic factor; A, additive genetic factor; L, liability for phobic fear. Latent factors can be correlated within families and influence the liability to phobic fearFig. 2Distribution of fear scores in 1997 for men and women age 14–25 and age 26–65 years. The Y-axis gives the percentage of males and females with each score. The dashed lines indicate the thresholds
Genetic structural equation modelling was carried out in Mx (Neale et al. 2003). Testing of submodels was done by means of likelihood-ratio tests, by subtracting the negative log likelihood (−2LL) for the more restricted model from the −2LL for the more general model. This yields a statistic that is distributed as χ2 with degrees of freedom (df) equal to the difference in the number of parameters in the two models. If the χ2-test yields a P-value higher than 0.05, the constrained model is deemed not significantly worse than the previous model and is kept as the most parsimonious model to which the next model will be compared.
We started with fitting saturated models to describe the correlational structure between twin pairs and between siblings in each zygosity group. We used repeated measures from two time points (Rebollo and Boomsma 2006). Twin and sibling correlations were constrained to be equal at the two time points. For each fear subtype (blood-injury, social, and agoraphobic fear) and within each age group (14–25 and 26–65 years), we estimated five twin correlations (one for each sex by zygosity group) and three sibling correlations (male–male, female–female, and male–female sib pairs). Two cross-time within person correlations (one for each sex) and eight cross-twin cross-time correlations (one for each of the five sex by zygosity and the three sib pair groups) were estimated for each trait (not reported in the results section). Thresholds were estimated separately for men and women at each time point. The spouse correlations (Spearman’s correlation coefficients) were calculated using SPSS 12.0 for windows.
First, we tested whether the resemblance in sibling pairs was the same as in dizygotic twin pairs. Next, quantitative sex differences were investigated by constraining the correlations for same-sex male twin pairs and same-sex female twin pairs to be equal. Qualitative sex differences were investigated by constraining the correlations for DZ same-sex twin pairs and DZ opposite-sex twin pairs to be equal.
In a genetic model, the influence of latent factors A, D and E on the phobic fear score in 1997 and 2000 was estimated by the parameters (factor loadings) a, d, and e. The estimates for a, d and e were constrained to be equal in 1997 and 2000 for all three fear subtypes because we assume that the same etiological mechanism influences phobic fear at both time points. Correlations between the latent A and D factors were fixed at 1 for MZ twins. For DZ twins and siblings, the correlations between the latent A factors were fixed at 0.5, because they share on average half of their segregating genes. Dominant genetic effects result from the interaction or combination of alleles at a particular locus. Offspring receive only one allele from each parent and not a combination of two alleles. Parents and offspring share on average half of their genes so the chance that two siblings receive the same allele is 0.5 × 0.5 resulting in a correlation of 0.25 between the latent D factor for DZ twins and sibling pairs.
We first tested whether a, d and e in the two age groups could be constrained to be equal. Next, different models (ADE, AE and E) were fitted to the data to test which sources of variance contribute to individual differences in phobic fear.
Results
Figure 2 shows the distribution of fear scores in 1997 for males and females aged 14–25 and aged 26–65. The 2000 scores (not presented here) show a similar pattern. Women had significantly more often high scores on each fear dimension than men in each age group, except for blood-injury fear in the oldest age group for which there was no difference between men and women. There was no effect of age on the distribution of fear scores, except for women’s scores on blood-injury fear, which were significantly higher in the youngest age group.
There was no significant correlation between twins and their spouses for blood-injury fear (rs = 0.05, P = 0.162), social fear (rs = 0.04, P = 0.324) or agoraphobic fear (rs = 0.04, P = 0.341).
Table 2 displays the twin correlations and confidence intervals for blood-injury, social, and agoraphobic fear for both age groups. Correlations for same sex DZ and DOS twin pairs, and for sibling pairs could be constrained to be equal, suggesting that there is no evidence for a special twin environment. MZ male–male and female–female correlations were the same and DZ same-sex correlations were also the same, indicating that there are no quantitative sex differences in the influence of genetic effects on the three fear types. The DZ opposite-sex twin correlation was equal to the same-sex DZ correlation indicating that the same genes affect fear in men and women. After these constraints, MZ correlations were more than twice as large as the correlations for DZ twins and siblings suggesting that genetic effects contribute to individual differences in phobic fear and that these effects act in a partly non-additive manner.
Table 2Twin and sibling correlations for blood-injury, social and agoraphobic fear for age group 14–25 years and age group 26–65 years (95% confidence intervals added in parentheses)Blood-injury fear Social fearAgoraphobic fearAge 14–25Age 26–65Age 14–25Age 26–65Age 14–25Age 26–65MZ males0.30 (0.17–0.42)0.36 (0.14–0.54)0.53 (0.41–0.63)0.29 (0.03–0.50)0.47 (0.34–0.56)0.49 (0.27–0.65)DZ males0.04 (−0.16–0.22)−0.04 (−0.32–0.25)0.15 (−0.05–0.34)0.49 (0.20–0.68)0.27 (0.09–0.43)0.22 (−0.10–0.48)MZ females0.34 (0.25–0.43)0.41 (0.32–0.50)0.48 (0.39–0.56)0.42 (0.31–0.52)0.40 (0.30–0.48)0.37 (0.26–0.47)DZ females0.18 (0.05–0.30)0.11 (−0.06–0.28)0.20 (0.06–0.33)0.37 (0.20–0.53)0.13 (−0.00–0.26)0.27 (0.08–0.44)DZ opposite sex0.23 (0.10–0.34)0.16 (−0.04–0.34)0.22 (0.10–0.34)0.25 (0.03–0.44)0.17 (0.04–0.29)0.22 (0.01–0.41)Brother–brother0.16 (0.03–0.28)0.28 (0.10–0.44)0.12 (−0.02–0.25)0.19 (−0.03–0.38)0.09 (−0.04–0.22)−0.01 (−0.24–0.22)Sister–sister0.10 (−0.01–0.20)−0.02 (−0.16–0.13)0.09 (−0.03–0.20)0.14 (−0.01–0.28)0.10 (−0.01–0.19)0.12 (−0.03–0.26)Brother–sister0.10 (0.02–0.18)0.06 (−0.05–0.17)0.15 (0.06–0.23)0.11 (−0.02–0.23)0.08 (−0.00–0.02)0.07 (−0.05–0.18)All MZa0.33 (0.26–0.40)0.39 (0.30–0.48)0.50 (0.43–0.56)0.40 (0.30–0.49)0.42 (0.35–0.49)0.39 (0.30–0.48)All DZ/siblingsa0.13 (0.08–0.18)0.09 (0.02–0.16)0.15 (0.10–0.20)0.20 (0.13–0.27)0.11 (0.07–0.16)0.12 (0.05–0.19)MZ = monozygotic; DZ = dizygoticaAfter constraining these correlations to be equal
Model fitting results for blood-injury, social, and agoraphobic fear are shown in Table 3. The data from two age groups were simultaneously analysed in one model with different parameter estimates for the two age groups. For each fear subtype, a general ADE model (model 1) was fitted. Next, we tested if the A, D, and E components were the same for the two age cohorts (model 2). Finally, in model 3, and in the case of social fear in models 4 and 5, the significance of D and A in the two age groups was tested by constraining the relevant parameters at zero. For blood-injury and agoraphobic fear, this resulted in a model in which variance components did not differ between the two age groups. Non-additive genetic effects could not be left out of the model for blood-injury and agoraphobic fear. A, D and E explained respectively 9.9, 25.6, and 64.5% of the variance in blood-injury fear, and 10.7, 29.9, and 59.5% of the variance in agoraphobic fear. For social fear in the youngest age group, A, D and E contributed to the variance, and explained 7.8, 42.9, and 49.3% of the variance, respectively, while in the oldest age group D did not contribute to the variance. In fact, 40.3% of the variance in social fear in the oldest age group could be explained by additive genetic effects and 59.7% by unique environmental effects (Table 4).
Table 3Model fitting results for blood-injury, social, and agoraphobic fear; comparisons of models are shownModelsvs−2LLdfΔdfΔχ2PBlood-injury fear1. ADE20254.5896082. ADE no age diff.120257.23961792.650.983. AE no age diff.220415.2796203158.030.00Social fear1. ADE19439.7296082. ADE no age diff.119468.909617929.180.003. AE youngADE old119454.059611314.330.004. ADE youngAE old119439.72961130.001.005. ADE youngE old419521.589614381.870.00Agoraphobic fear1. ADE 19967.6196142. ADE no age diff.119973.66962396.050.743. AE no age diff.219984.909626311.240.01vs = versus and indicates which model the sub model is compared to; −2LL = −2 log likelihood; df = degrees of freedomMost parsimonious solution per fear subtype is shown in boldTable 4Parameter estimates of the best-fitting models in age group 14–25 and age group 26–65 for blood-injury fear, social fear and agoraphobic feara2 (%)d2 (%)e2 (%)Blood-injury fearAge 14–659.925.664.5Social fearAge 14–257.842.949.3Age 26–6540.3–59.7Agoraphobic fearAge 14–6510.729.959.5a2 = additive genetic variance component; d2 = dominant genetic variance component ; e2 = unique environmental variance component
Comments
This is the first study that explored the genetic and environmental contributions to individual differences in blood-injury, social, and agoraphobic fear using an extended twin design. We tested whether there was evidence for assortative mating, for a specific twin environment, and if the impact of the genetic and environmental factors differed between men and women and between two age cohorts.
We observed that more women than men scored high on all fear dimensions, except for blood-injury fear in the oldest age group. This pattern is consistent with the large majority of studies that looked at sex differences in fear levels: overall, women tend to have higher scores on self-report measures tapping various fear categories, the exception being blood-injury phobia, where the sex distribution is somewhat more balanced (Bijl et al. 1998; Kessler et al. 1994; Middeldorp et al. 2005).
Prior studies showed that many fear types display a distinct developmental course, with, in general, a decline of fear levels from adolescence to later life. Our data suggest no large effect of age in fear scores: a pattern of declining fear scores was only seen for blood-injury fear, with younger participants having higher fear levels than older participants. This is in accordance with the literature (Bijl et al. 2002; Page 1994). However, many developmental changes in fear levels occur before the adolescent stage (Marks 1987), an underrepresented age group in our sample. Future research will have to determine whether changes in blood-injury, social and agoraphobic fear emerge before adolescence.
Spouse similarity is an important issue to consider when studying genetic influences on population variation. In accordance with what was reported previously for phobias (Maes et al. 1998) and anxiety disorders (Low et al. 2007), we did not find a significant association between spouses, indicating that there is random mating for blood-injury, social and agoraphobic fears.
Broad-sense heritability estimates for blood-injury, social, and agoraphobic fear were moderate ranging from 35.5% (blood-injury fear) to 50.7% (social fear in the youngest age group). These heritability estimates for blood-injury fear and agoraphobic fear are comparable to the estimates reported by Kendler et al. (1992, 2001). The heritability estimate for social fear (50.7% in the youngest age group and 40.3% in the oldest age group) was somewhat higher than that reported by Kendler et al. (30% for women and 20% for men) but comparable to the heritability estimate found by Middeldorp et al. (2005) (50%). Contrary to what was found by Hettema et al. (2005, 2006) shared environmental factors did not significantly contribute to the variance in social fear. All in all, these results confirm the conclusion of Kendler et al. (1992, p. 279) that “the estimated heritability of liability for phobias indicates that genetic factors play a significant, but by no means overwhelming role in the etiology of phobias.”
In accordance with previous findings (Hettema et al. 2005; Middeldorp et al. 2005; Kendler et al. 2002), no differences in heritability between men and women were observed. Regarding qualitative sex differences, the results are somewhat different from previous studies. Whereas results of Kendler et al. (2002) and Middeldorp et al. (2005) suggested the presence of qualitative sex differences for agoraphobic fear, we did not find any evidence that different genes influence the liability of phobic fear in men and women. Since the sample in our study was enriched with siblings of twins, a large number of opposite sex first degree relatives was added to the study. These data do not indicate a lower familial correlation in opposite-sex relatives.
For all three fear subtypes, MZ correlations were more than twice as high as the DZ/sibling correlations, indicating that non-additive genetic effects may contribute to the variance in phobic fears. Several previous studies suggested the influence of non-additive genetic effects on fears and phobia, which makes sense because fears are likely to be exposed to selection during evolution. Skre et al. (2000) investigated the genetic and environmental contributions to common phobic fears in a treatment sample of 61 twin pairs and found the twin correlations for social fear to fit best to a model including non-additive genetic effects. For blood-injection-injury fear, the correlational structure found by Skre et al. suggested the influence of non-additive genetic effects but model fitting showed that due to violation of equal variances between MZ and DZ twins all variance was accounted for by unique environmental effects. Neale et al. (1994b) also reported evidence for non-additive genetic effects in reanalysing the Rose and Ditto data (1983) of common social fears. In addition, Kendler et al. (1999) found evidence for non-additive genetic effects in agoraphobia, but not in social phobia and blood-injury phobia, while Page and Martin (1998) suggested the possibility of non-additive genetic effects on the variance in blood-injury phobia. In 2002, Kendler et al. (2002) did not model non-additive genetic effects explicitly, yet the correlation structure did suggest the presence of non-additive genetic effects for agoraphobia and blood-injury phobia. To summarize, many studies indicated the presence of non-additive genetic effects on the liability for fear and phobias but they often did not have sufficient statistical power to detect non-additive genetic effects. The large sample size of this study together with repeated measures enabled us to detect the influence of non-additive genetic effects on blood-injury fear, social fear (in the youngest age group), and agoraphobic fear, confirming the speculations discussed in previous studies. For social fear in the oldest age group, non-additive genetic effects were not detected. Thus, the mixture of genetic and environmental influences on social fear seems to differ between the two age cohorts, though the differences in correlation structure were small. These results could be relevant to gene mapping of anxiety and phobia. If many of the genes underlying anxiety and phobia have non-additive effects, as suggested by our results, then quantitative trait loci with little additive genetic effects may have been missed in linkage analysis (Purcell and Sham 2004). Explicitly modelling non-additive genetic effects may therefore be important in the linkage analysis of anxiety, fear and phobia.
In general, our data are well in line with the multifactorial model of fears and phobias (Muris and Merckelbach 2001). This model assumes that a genetic vulnerability predisposes individuals to develop maladaptive fears and that discrete learning experiences (i.e., unique environmental factors) in combination with genetic vulnerabilities produce persistent fears.
Results of this study should be interpreted in context of the following limitations. First, the present study relied on non-clinical groups, and, although there is no reason to assume that heritabilities increase at the extreme end of the fear continuum (Stevenson et al. 1992), it is important to replicate the findings in clinical samples. Second, data of our study were obtained with an instrument (i.e., the FQ) that primarily focuses on self-reported avoidance behavior related to a limited number of fear categories and our results may not be generalized to other specific fears such as fear of small animals.
In summary, we found random mating for blood-injury, social and agoraphobic fear. Individual differences in blood-injury fear and agoraphobic fear in both age groups and social fear in the youngest age group (14–25 years) could be explained by additive and non-additive genetic factors and unique environmental factors, while individual differences in social fear in the oldest age group (26–65 years) could be explained by additive genetic and unique environmental factors. The heritability of the three fear subtypes was similar for men and women and no effects of sex-specific genes were detected.

fear
genetics
dominance
assortative mating
twin-family study