Title: Effects of Prenatal Alcohol Exposure on Ridge Counts and Patterns of the Fingertips.

Transcription

1 Tit: Effects of Prenatal Alcohol Exposure on Ridge Counts and Patterns of the Fingertips. Authors and Institutions: Darryl J. Holman 1 Emily K. Brunson 1 Siobhán M. Mattison 1 Laura L. Newell-Morris 1 Ann Striessguth 2 1 Department of Anthropology 2 Department of Psychiatry and Behavioral Sciences University of Washington Seatt, WA 9819 Total Number of Pages: Number of Figures: Number of Tabs: Abbreviated tit: Prenatal Alcohol Exposure and Fingertip Dermatoglyphics Send Proof to: Darryl J. Holman Department of Anthropology Box 100 University of Washington Seatt, WA USA Fax: (206) djholman@u.washington.edu Keywords: prenatal alcohol exposure, fingertip patterns, fingertip ridge counts

2 2 Holman, et al. ABSTRACT The abstract goes here.

3 Holman, et al. Normal fetal development necessitates the absence of abnormal stressors in uterine environments. When a developing fetus is exposed to additional sources of stress, its development may be delayed, altered, or even terminated. The specific outcome that results depends on several factors including the nature of the stressor; the timing, duration and extent of the exposure; and the overall quality of the uterine environment. One factor that remains constant, however, is that once an abnormal stressor has impacted the fetus the impact is irreversib. Research over the past four decades has conclusively shown that alcohol acts as a significant stressor in uterine environments (xx). Fetuses that are exposed to alcohol in utero are at risk for developing several neurological, growth and behavioral abnormalities that can be loosely classified as fetal alcohol spectrum disorder (FASD) (xx). As a descriptive tool, FASD encompasses several diagnoses including fetal alcohol syndrome (FAS) and fetal alcohol effects (FAE). FAS is a clinical diagnosis that is characterized by central nervous system dysfunctions, pre- and/or post-natal growth retardation, and at ast 2 abnormal craniofacial features such as epicanthal folds or smooth philtrums (xx). Its symptoms result in long term physical and mental probms for the individuals afflicted with them. FAE is a diagnosis reserved for individuals who have some but not all of the features of FAS. Whi clinically deemed ss severe, research has suggested that the disabilities of peop diagnosed with FAE are no ss severe than those diagnosed with FAS (Streissguth et al., 1996). Whi a significant amount of research has been conducted in the past to assess the post-natal effects of prenatal alcohol exposure, current efforts have resulted in an even greater understanding of the impacts that alcohol exposure has on developing fetuses (see xx, xx, and xx for examps). The purpose of this study is to add an additional line of evidence to this literature by assessing how alcohol exposure in utero affects the development of two specific dermatoglyphic traits: fingertip pattern types and fingertip ridge counts. The term dermatoglyphics refers to the system of skin ridges and their patterns that cover the volar surfaces of the hands and feet. The template for these ridges develops between weeks 10 and 17 of gestation, a timeframe that also includes critical periods of central nervous system development (xx). Like development of the central nervous system, development of dermatoglyphics has been shown to be influenced by genetic factors, as well as prenatal environmental stress. Previous research on the impact of in utero alcohol exposure, for examp, has suggested that alcohol impacts both the development of palmar creases (Jones, 197) and a-b ridge counts (Kheun et al., 2001; Wilbur et al., 199). Unlike the central nervous system, however, once the templates for skin ridges are compted, they remain unchanged after that time. In other words the configuration of epidermal ridges are... a history of the developmental period during which the ridges form (Babr, 1991 p. 9). For this reason, and because they are easily accessib, dermatoglyphic traits offer a unique opportunity to assess in utero alcohol exposure that occurred between weeks 10 and 17 of gestation. MATERIALS AND METHODS Dermatoglyphic data were colcted from a total of 87 individuals, 20 of whom were diagnosed with FASD. These individuals had been referred to the Fetal Alcohol Syndrome Diagnostic Clinic at the University of Washington for evaluation and enrold in a follow-up study from 1994 to 199. At the time of recruitment, ages of these subjects ranged from 6 to 1 years. An additional 184

4 4 Holman, et al. individuals were then recruited as unexposed controls from undergraduates and staff at the University of Washington between 1996 and These individuals were screened for eligibility through interviews. Control participants reported no prior diagnoses of FASD, no familial histories of heritab diseases or congenital defects, and that their mothers did not consume alcohol and were not likely to have done so during their pregnancies. Ages in the control group ranged from 19 to years. Handprints from both groups, including fingerprints, were taken using the standard carbonpaper-tape method (Aase and Lyons 1971). Prints were then digitally scanned at a resolution of 400 dots per inch. Finger dermatoglyphics, specifically fingertip pattern types and ridge counts, were read in Adobe Photoshop (version 8.0, Abobe Systems, Inc., San Jose, CA). After all of the prints were read, case numbers were used to identify individual characteristics including race, sex and diagnosis (FASD or control). Dermatoglyphic Traits Fingerprint pattern types were assessed following the methods of Elbualy and Schinder (1971). One reader (SMM) qualitatively assigned one of five pattern classifications arch, ulnar loop, radial loop, doub loop and whorl (Figure 1) to each finger based on the overall appearance of the print. Assessments of fingertip patterns were independent and conducted in a random order; patterns from different fingers on the same hand were read independently, and right and ft fingerprint patterns from the same individual were not assessed at the same time. Pattern type assessments were later verified by a second reader (EKB). Ridge counts of each fingertip were also assessed by EKB, again following the methods established by Elbualy and Schinder (1971). For this process, prints were digitally enlarged so that fingertip triradii could easily be marked. Straight lines were then drawn between these triradii and the center of the fingertip pattern (Figure 2); a count was then made of the number of ridges that crossed or touched this line. When a triradius was not present on a fingerprint, which occurred when an arch pattern type was present, no ridge count was recorded for that finger. Conversely, when two triradii were present, as occurred when either doub loop or whorl pattern types were present, two ridge counts were recorded for that finger. As with fingertip patterns, readings of fingertip ridge counts were read independently and in a random order. Data Analysis Using MLE we developed a statistical model that, for each digit, simultaneously estimated the probability of each fingerprint pattern type occurring as well as the distribution for the associated ridge count(s) that arose under each pattern type. This method offered a significant improvement over previous work that has negcted the influence of pattern type on ridge counts by relying on sums or averages of fingertip ridge counts (total ridge counts, absolute ridge counts, and/or average ulnar or radial ridge counts) instead of actual ridge count measurements for each finger on a hand. This new model also allowed the effects of covariates, including FASD diagnosis, to be moded on both the probability of each pattern type and on the ridge count(s) specified by that pattern directly. Because this analysis was not concerned about asymmetry within individuals, and because using data from both hands of individuals violated the model s assumption independence for the digits, only data from right hand prints were used in the analysis. Right hands were chosen because there were fewer cases of missing data for right hands compared to ft hands in the samp.

5 Holman, et al. Pattern type probabilities: A multinomial logit model was used to estimate the probability that each pattern type would arise on a particular finger (Figure ). The likelihood for the multinomial pattern probabilities and ridge-counts for N subjects for a given digit was: (1) L= N i= 1 { gr ( A, ßr ) A δ( π i= A) gr ( DL, ßr ) f (,, ) (,, ) DL radialdl cradial a i radialdl bradialdl ßradialDL fulnardl culnar a i ulnardl bulnardl ßulnarDL gr ( W, ß r ) f (,, ) W radialw cradial a i radialw bradialw ßradialW f ( c a, b, ß ) i ulnarw ulnar ulnarw ulnarw ulnarw gr ( RL, ßr ) f (,, ) (,, ) RL radialrl cradial a i radialrl bradialrl ßradialRL fulnarrl culnar a i ulnarrl b ulnarrl ßulnarRL δ( π i= W ) δ( πi= DL) δ( πi= RL) } where π I is the ith individual s pattern for the digit (A is arch, DL is doub loop, W is whorl, and UL is ulnar loop) and the function δ() returns 1 if the argument is true and 0 otherwise.. The function g() is an inverse logit transformation, g(r, b ) = 1/[1 + exp(r + x i b)]. Ridge counts: We also estimated parameters for a lognormal distribution of ridge counts for each pattern type. A two-parameter lognormal probability density function (PDF) was given by: (2) [ ln( t a) ] 1 2 2b f ( t a, b) = e. tb 2π 2 with of mean of aexp(b 2 /2), and a variance of a 2 exp(b 2 /2)[exp(b 2 /2) 1] (Evans et al., 2000). The basic ridge-count model (ignoring the effects of covariates) included two sets of parameters for both whorl and doub loop pattern types one for ridge counts that would correspond to radial ridge counts and one for ridge counts that would correspond to ulnar ridge counts but only one set of parameters for ulnar loop pattern types. Because arches have no ridge counts, fingers with this pattern type were excluded from this part of the analysis. Covariates: We examined the effect of three categorical covariates on the probabilities of each pattern type occurring, as well as on the ridge counts associated with each of these pattern types. The covariates were diagnosis (unaffected = 0, FASD = 1), sex (0=, 1=ma), and ethnicity ( = 0 and = 1). We moded the effects of covariates as affecting the distribution of risk, analogous to the hazard in survival analysis. Mathematically, for the i-th subject, the model includes a 1 vector of covariates, x i = (x 1i, x 2i,..., x Mi )', and coefficient effects for each type of ridge count b = (β 1, β 2,..., β M )'; the effects of multip covariates are moded as log-linear effects on the risk of ridges (see Holman and Yamaguchi 200).

6 6 Holman, et al. Model Analysis: Maximum likelihood estimates of the parameters in equation 1 were found using m version 2.1 (Holman, 200). The m program and the code to do the analysis are availab from DJH. Standard errors of mean emergence times (SEMs) were computed numerically using the delta method (Elandt-Johnson and Johnson, 1980). Akaike Information Criterion (AIC) was then used to sect the most parsimonious set of covariates (Akaike, 197, 1992; Burnham and Anderson, 1998). This criterion is computed as twice the negative loglikelihood added to twice the number of parameters in the model. The model that minimizes AIC was taken to be the most parsimonious model; that is, the model exhibiting the best trade-off between goodness-of-fit and a minimum number of parameters. RESULTS Fingertip pattern types and their corresponding ridge counts were not ab to be read in all cases. Out of the 87 right hand prints included in the samp, thumb, 2 index finger, 4 midd finger, ring finger and 4 pinky finger prints were either missing or were too distorted to be read. These were subsequently dropped from the analysis, aving between 8 and 8 prints per finger included in the samp. Frequencies of fingertip pattern types varied by finger (Tab 1), however, ulnar loops were always the most common pattern type and radial loops the ast common. Further analysis also revead that the probabilities of fingertip pattern types were impacted by FASD diagnosis, race and sex, and that this also varied by finger (Tab 2). FASD diagnosis affected pattern type probabilities on the thumb, index, ring and pinky fingers but not on the midd finger. Race impacted the probabilities of pattern types on the index, midd, ring and pinky fingers but not on the thumb. Finally, sex had an effect on the pattern type probabilities on the index, ring and pinky fingers but not on the thumb or the midd finger. The most striking impact of these covariates occurred on the index finger where, for examp, individuals diagnosed with FASD were much more likely to have doub loops, whorls, ulnar loops and radial loops compared to their FASD counterparts who conversely were much more likely to have arch pattern types. Data on fingertip ridge counts were directly associated with fingertip pattern types. Arches, which lack triradii, gave no ridge count measurements; ulnar and radial loops each gave one ridge count measurement; and doub loops and whorls each gave two ridge count measurements. Average ridge counts associated with each of the different pattern types can be found in Tab (corresponding parameters of the most parsimonious model can be found in Tab 4). In general, ulnar counts were always higher than radial counts. This was true when comparisons were made between the ulnar and radial ridge counts of doub loops or whorls, and when the ridge counts of ulnar loops and radial loops were compared to one another. Additionally, average ridge counts tended to be larger on the thumb compared to the other fingers. When considered in light of FASD diagnosis, race and sex, ridge count measurements like fingertip pattern types were impacted, but the effect of these variabs depended on both the pattern type and the finger. FASD diagnosis affected ridge count measurements only on the pinky finger, where a positive FASD diagnosis was associated with a lower average ridge count measurement (14 compared to 12. for FASD controls). Race affected average ridge counts on the thumb, ring

7 7 Holman, et al. and pinky fingers, but only on the ulnar ridge counts of whorls (thumb, ring finger) and ulnar loops (pinky finger). Finally, sex had no effect on average ridge counts on any finger. DISCUSSION One of the greatest difficulties associated with early FASD diagnoses is that detection is often difficult at early ages because not all features of FASD may be apparent at the time (Stor and Holmes). Recent studies suggest that factors associated with better outcomes for individuals exposed to alcohol in utero are early diagnosis, a stab living environment, and eligibility for special services, which in turn is largely dependent on early diagnosis of the disorder. Currently research suggests that dermatoglyphic traits may prove useful when other clinical diagnostic methods are in doubt (Babr, 1991). Like many developmental characteristics, dermatoglyphic traits, including palmar ridge counts, atd angs, palmar crease patterns, fingertip pattern types, and fingertip ridge counts, are sensitive to intrauterine disturbances, including those caused by abnormal stressors. We examine the effects of alcohol exposure in utero on fingertip pattern type as well as the effect of alcohol exposure in utero on fingertip ridge counts, controlling for the pattern type. Whi we found a small and consistent effect of alcohol exposure on the ridge counts in the pinky finger, the effects were small and are unlikely to prove useful in diagnosing in utero alcohol exposure. CONCLUSION ACKNOWLEDGMENTS We thank Carrie Kuehn for her work in colcting some of the prints used in this study. This research was supported by grants from NIH NIAAA (R21-AA01704 and R7-AA014) and a grant from the Alcohol and Drug Abuse Institute, University of Washington.

8 8 Holman, et al. Tab 1. Frequencies of pattern type on the right hand by finger. thumb index midd ring pinky n (%) n (%) n (%) n n (%) arch 16 (4.1) 0 (12.9) 26 (6.7) 7 (1.8) 8 (2.1) doub loop 70 (18.1) 28 (7.2) 6 (1.6) 14 (.6) 6 (1.6) whorl 96 (24.8) 96 (24.8) (14.2) 148 (8.2) 46 (11.9) ulnar loop 199 (1.4) 147 (7.9) 289 (74.7) 208 (.7) 21 (82.9) radial loop (8) 64 (16.) 7 (1.8) 7 (1.8) 2 () missing (8) 2 () 4 (1.0) (8) 4 (1.0) Tab 2. Frequencies of pattern type on the right hand by finger. thumb index midd ring pinky n (%) n (%) n (%) n n (%) arch 17 (4.0) 9 (1.8) 27 (6.) 8 (1.9) 9 (2.1) doub loop 77 (18.0) 0 (7.0) 7 (1.6) 16 (.7) 6 (1.4) whorl 10 (24.1) 10 (24.1) 64 (1.0) 161 (7.7) 49 (11.) ulnar loop (7) 7 (17.1) 8 (1.9) 9 (2.1) 2 () radial loop 22 (2.2) 19 (7.2) 16 (74.0) 229 (.6) 6 (8.4) missing 4 (9) (7) (1.2) 4 (9) (1.2)

9 Tab 2. Probabilities of finger pattern types on the right hand by diagnosis, sex and race. diagnosi arc whor S ulnar finger s sex race h SE doub loop SE l E loop SE radial loop SE 1 thumb normal FASD index normal normal normal ma normal ma FASD FASD FASD ma

10 10 Holman, et al FASD ma midd ring normal normal normal ma normal ma FASD

11 11 Holman, et al. 2 FASD FASD ma FASD ma pinky normal normal normal ma normal ma FASD FASD

12 12 Holman, et al. 2 FASD ma FASD ma Tab 4. Average ridge counts of right hand finger pattern types by diagnosis, sex and race. µ µ µ µ diagn ulnar radial S ulnar radial finger osis race dl SE SD dl SE D w SE SD w SE SD µ ul SE DL µ rl SE SD thumb index midd

13 1 Holman, et al ring norm pinky al norm al FAS D FAS D

14 Tab 4. Results of most parsimonious models for right finger patterns and counts. thumb index midd ring pinky n est SE est SE est SE est SE est SE ra bra_diag bra_sex bra_race rdl brdl_diag brdl_sex brdl_race muudl sigudl murdl sigrdl bdiagudl bsexudl braceudl bdiagrdl bsexrdl bracerdl rw brw_diag

15 1 Holman, et al. brw_sex brw_race muuw siguw murw sigrw bdiaguw bsexuw braceuw bdiagrw bsexrw bracerw rul brul_diag brul_sex brul_race muul sigul bdiagul bsexul braceul murl sigrl

16 16 Holman, et al. bdiagrl bsexrl bracerl AIC

17 17 Holman, et al. FIGURE CAPTIONS Figure 1. Examps of fingertip pattern types: (a) is an arch, note the lack of a triradius; (b) is a loop; (c) is a doub loop; and (d) is a whorl. Figure 2. Examps of ridge count measurements for different fingertip pattern types. The arrows point to the triraduis/triradii present in the pattern. The lines are drawn between the triradii and the center of the patterns. Ridges that cross these lines would be counted as part of the ridge count measurement. Figure. Probability tree showing the contributions to the likelihood for each finger pattern. The r parameters are conditional probabilities of traversing each branch. The probability of each pattern arising is given as probability p, computed from the rs. The f()s are probability density functions for the radial and ulnar ridge count for each specific pattern. The arch has no ridge counts.

18 18 Holman, et al.

19 19 Holman, et al.

20 20 Holman, et al. r A 1 - r A r DL 1 - r DL r W 1 - r W r UL 1 - r UL Arch Doub loop Whorl Ulnar loop Radial loop p A = r A p DL = r DL (1 - r A ) p W = r W (1 - r DL ) p UL = r UL (1 - r W ) p RL = (1 - r UL ) (1 - r W ) (1 - r A ) (1 - r DL ) (1 - r A ) (1 - r DL ) (1 - r A ) f radialdl (c radiali a radialdl, b radialdl ) f ulnaldl (c ulnari a ulnardl, b ulnardl ) f radialdl (c radiali a radialdl, b radialdl ) f ulnaldl (c ulnari a ulnardl, b ulnardl ) f radialdl (c radiali a radialdl, b radialdl ) f ulnaldl (c ulnari a ulnardl, b ulnardl ) f radialdl (c radiali a radialdl, b radialdl ) f ulnaldl (c ulnari a ulnardl, b ulnardl )