Fingerprint Ridge-Count Difference between Adjacent Fingertips (dR45) Predicts Upper-Body Tissue Distribution: Evidence for Early Gestational Programming

Henry S. Kahn1, Roopa Ravindranath2, Rodolfo Valdez1 and K. M. Venkat Narayan1

1 Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.
2 Department of Anatomy, St. John's Medical College, Bangalore, India.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Fingerprint ridge counts, which remain constant from the 19th week of pregnancy, are related to fingertip growth during early gestation. Each finger corresponds neurologically to a spinal-cord segment ranging from C6 (thumb, relatively cephalad) to C8 (fifth finger, relatively caudad). The authors hypothesized that large ridge-count differences between fingertips (cephalad > caudad) might reflect fetal inhibition of caudal growth. Among 69 male Atlanta, Georgia, military recruits (1994–1997; aged 17–22 years), they tested associations of the anthropometric waist-to-thigh ratio with 20 ridge-count differences. Waist-to-thigh ratio was associated with the ridge-count difference between the right fourth and fifth fingertips only (dR45; r = 0.36, p = 0.003). The race-adjusted standardized regression coefficient was 0.22 (95% confidence interval: 0.03, 0.41). Since upper-body tissue distribution indicates disease risk, the authors then tested the association of age (an indicator of survivorship) with dR45 in a sample of 135 male patients from Bangalore, India (1989–1990; aged 38–82 years). Age was inversely associated with dR45 (r = -0.17, p = 0.04), notably among the 75 men with diabetes (r = -0.22, p = 0.06). An increased dR45 predicts an upper-body tissue distribution originating before the midpoint of pregnancy. The cause of this developmental pattern is unknown, but it may lead to reduced survivorship.

age factors; anthropometry; body constitution; dermatoglyphics; diabetes mellitus; risk factors; survival rate

Abbreviations: CI, confidence interval; dR15, difference between the ridge counts of the first and fifth fingers of the right hand; dR45, difference between the ridge counts of the fourth and fifth fingers of the right hand; WTR, waist-to-thigh ratio of circumferences


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Circumstances during gestation may affect subsequent adult metabolism as well as adult body size. A baby of reduced birth size, for example, carries an increased risk of insulin resistance in later life (1GoGoGoGo–5Go). The mechanisms that underlie this prenatal, metabolic programming have not been established, and there is uncertainty about precisely when during gestation a fetal insult must occur to affect adult health (6GoGo–8Go). Some reports suggest a relatively early time window in which pertinent gestational exposures may occur (9GoGoGoGoGoGoGoGo–17Go).

If an adverse fetal exposure were to occur before the middle of gestation (i.e., before week 20), we hypothesized that evidence of this effect might be found in the fingerprints of the offspring. A human fingerprint is the representation of dermal ridges on each fingertip. These dermal ridges are formed during gestational weeks 12–19, and the resulting fingertip ridge configuration (i.e., the fingerprint) is fixed permanently before the midpoint of pregnancy (18Go, 19Go). On each fingertip, the number of primary dermal ridges (the "ridge count") provides a measure of fingertip growth activity during the early fetal period (18Go, 20Go).

We reasoned that the size of a fetal fingertip (compared with its neighbor on the same hand) might be influenced by factors that stimulate or inhibit growth along the developmental axis extending from the brain to the lower limbs. Each fingertip is related neurologically to a spinal-cord segment in a range that includes the sixth through the eighth cervical levels (i.e., C6–C8; figure 1). The first finger (thumb) is linked to the cephalad (upper) side of C6, and the fifth finger is linked to the caudal (lower) side of C8 (21Go). We hypothesized, therefore, that a large ridge-count difference between fingers on the same hand (a decline in the cephalocaudal direction) might reflect circumstances associated with relative inhibition of caudal growth. An embryo or fetus developing under such conditions might accumulate relatively less tissue in the lower body. Decades later, his or her adult habitus might demonstrate reduced lower-extremity muscle mass and a tissue distribution that favored the upper body.



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FIGURE 1. Schematic illustration of the innervation of the fingers, demonstrating anatomic correlations with the sixth through the eighth cervical spinal-cord segments (C6–C8). Adapted from Heimer (21Go).

 
This paper reports our finding among young men that the waist-to-thigh ratio (WTR) of circumferences, an index of upper-body tissue distribution, was associated with the arithmetic difference between ridge counts of the fourth and fifth fingertips of the right hand. This ridge-count difference is a continuous variable we designated dR45. Adults with large WTR values have adverse metabolic risk factors (22GoGo–24Go) and an increased risk of major chronic diseases (25Go, 26Go). Therefore, in a related exercise, we also wanted to learn whether dR45 would be inversely associated with age in a cross-sectional survey of middle-aged and older men. In such a sample, an inverse association with age would be consistent with an adverse effect on survivorship.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Our anthropometry study population consisted of 69 male recruits (49 Whites, 20 Blacks) who had just successfully completed their military entrance physical examination (in 1994–1997). They were aged 17–22 years and resided in or around Atlanta, Georgia. Weight and height were obtained without shoes or heavy clothing. After informed, written consent was given (protocol approved by the Emory University Human Investigations Committee, Atlanta, Georgia), circumferences were measured in the supine position (27Go) on an examination table or firm bed. The waist was measured midway between the lower ribs and the iliac crests during relaxed exhalation, and the midthigh was measured midway between the lateral inguinal fold and the middle of the patella on the right side. Circumference values for each subject were established as the mean of two independent measurements (four measurements if the first two values differed by >1 cm), and WTR was calculated as the mean waist value divided by the mean midthigh value.

By design, the study included only singleton births. The birth weight of 59 of our male recruits was retrieved from the standard Georgia birth certificate, and no reliable information was available on gestational age or birth length. We did not determine right- or left-handedness.

Fingerprints were obtained by using the ink-and-paper method of the US Department of Defense (28Go). Ridge counts for each fingertip were calculated from the number of primary dermal ridges that intersected or touched a straight line drawn from the central core of the fingerprint pattern to one or two adjacent triradial points (29Go, 30Go). Consistent with standard methods, fingertips with an arch pattern (panel A, figure 2) were assigned a ridge count of zero, and fingertips with a loop pattern (panel B, figure 2) received a ridge count equal to the number of ridges crossing the single straight line. For fingertip patterns with two triradial points (e.g., whorls, double loops; panel C, figure 2), we chose the following ridge-counting protocol: ridge count = (ridges crossing the longer line) + (1/2 of ridges crossing the shorter line).



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FIGURE 2. Examples of fingertip patterns representing an arch (panel A), loop (panel B), and whorl (panel C). Adapted from Holt (29Go). The following ridge-counting protocol was used: ridge count = (ridges crossing the longer line) + (1/2 of ridges crossing the shorter line); therefore, the ridge-count values in these examples are A = 0, B = 13, and C = 21 (i.e., 17 + (0.5 x 8)).

 
For each of 10 finger combinations on each hand, we computed the difference between ridge counts as the caudal ridge-count value subtracted from the cephalad ridge-count value (e.g., dR45 = fourth ridge count – fifth ridge count). We evaluated the associations between each of these ridge-count differences and WTR by comparing their respective Pearson's correlation coefficients. We then described the correlation between WTR and dR45 by calculating the standardized regression coefficient, that is, the change in the anthropometric outcome variable (expressed as a fraction of the sample standard deviation) for each standard deviation increase in dR45.

In a related exercise, we used fingerprints from a cross-sectional sample of adult male patients to test the association of age (a correlate of survivorship) with the dR45 ridge-count difference. As has been reported previously (31Go), 135 male patients were fingerprinted (in 1989–1990) at St. John's Medical College and Hospital and at Specialists Centre in Bangalore, India. They were aged 38–82 years, and approximately 85 percent were outpatients. Seventy-five of the 135 men had type 2 diabetes, established by glucose tolerance test and onset during adulthood. A screening questionnaire excluded participants with diseases or congenital abnormalities thought to be associated with fingerprint or other dermatoglyphic abnormalities. All participants provided consent for this study in accordance with a protocol approved by the Ethical Committee of St. John's Medical College. Fingerprints were obtained by using a conventional ink-and-paper method, and ridge-count values for each fingertip were determined in the same way as those in our anthropometry study of young Atlanta recruits. For this sample of middle-aged and older men, no data were available on anthropometry, behavioral risk factors, or right- or left-handedness.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Among the 20 possible ridge-count differences evaluated in our anthropometry study, only dR45 was significantly associated with WTR (r = 0.36, p = 0.0027) (table 1). The ridge-count difference demonstrating the next strongest association with WTR was dR15 (between the right first and fifth fingertips) (r = 0.23, p = 0.062). Birth weight was not significantly associated with either dR45 (r = -0.12, p = 0.38) or dR15 (r = -0.10, p = 0.46).


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TABLE 1. Correlations (Pearson's r) of waist-to-thigh ratio with all 20 differences between ridge counts{dagger} on specified fingers{ddagger} of 69 male military recruits, Atlanta, Georgia, 1994–1997

 
Ridge-count data from the right first, fourth, and fifth fingers, along with anthropometry values, are summarized in table 2. Race-ethnic contrasts were found for WTR and for ridge-count differences, suggesting the need to consider race-ethnicity as a potential confounding variable.


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TABLE 2. Reference values for 69 male military recruits, Atlanta, Georgia, 1994–1997

 
The slope (ß coefficient) for the association between dR45 and WTR was 0.0057 (95 percent confidence interval (CI): 0.0021, 0.0093), which indicates that for each 10-unit increase in dR45, WTR increased by about 0.057 units. For each standard deviation increase in dR45, the WTR value increased by 0.36 standard deviation (standardized regression coefficient, table 3). After adjustment for race, the ß coefficient for WTR was 0.0036 (95 percent CI: 0.0004, 0.0066) and the standardized regression coefficient was 0.22. The independent influences of dR45 on waist circumference and on thigh circumference were opposite but were of similar magnitude (table 3), a pattern compatible with the hypothesized effect on relative tissue distribution along the cephalocaudal axis. Race-stratified analyses suggested a comparable effect size among the 49 White and 20 Black military recruits.


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TABLE 3. Associations of the ridge-count* difference dR45{dagger} with the waist-to-thigh ratio, waist circumference, and midthigh circumference of 69 young, male military recruits, Atlanta, Georgia, 1994–1997{ddagger}

 
In our cross-sectional sample from Bangalore (table 4), we found that among the 135 male patients, dR45 was inversely associated with age (r = -0.17, p = 0.043). The variable dR15, however, showed no association with age (r = -0.01, p = 0.88). The ß coefficient for dR45 was –0.28 years (95 percent CI: -0.54, -0.01), indicating that for each 10-unit increase in dR45, average age decreased by about 2.8 years. This inverse association was slightly stronger for the 75 men with diabetes (r = -0.22, p = 0.061; ß = -0.37 years, 95 percent CI: -0.76, 0.01) than for the 60 nondiabetic men (r = -0.10, p = 0.47; ß = -0.13 years, 95 percent CI: -0.49, 0.23). We found no evidence of nonlinearity in any of these regression models. Figure 3 illustrates that among the diabetic men, mean age declined with increasing values of dR45 (F = 2.29, p = 0.11 for effect across three groups of dR45).


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TABLE 4. Reference values for 135 male medical patients, Bangalore, Karnataka, India, 1989–1990

 


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FIGURE 3. Cross-sectional associations between the ridge-count difference from the right fourth to fifth fingertips (dR45) and age among 75 diabetic male patients and 60 nondiabetic male patients in Bangalore, India, 1989–1990. Vertical bars denote one standard error of the mean.

 
To compare the fingerprint data from the Bangalore and Atlanta men, we considered only the younger Bangalore patients, that is, those young enough to experience a minimal effect of ridge-count differences on adult survivorship (aged 38–54 years; n = 58, including those with and without diabetes). Their mean dR45 value was 5.5 (standard error, 0.7), slightly larger than the mean for the 49 Atlanta White men (4.7, p = 0.48 for difference from Bangalore) and significantly larger than the mean for the 20 Atlanta Black men (1.6, p = 0.006 for difference from Bangalore). The mean dR15 value for the Bangalore men was 8.6 (standard error, 1.2), slightly smaller than that for the Atlanta White men (9.5, p = 0.60) and significantly larger than the mean value for the Atlanta Black men (4.2, p = 0.040).


    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Our anthropometric study of healthy young men identified a combination of two fingertips–those on the fourth and fifth fingers of the right hand–for which the ridge-count difference (dR45, a continuous variable) was significantly correlated with WTR. This ridge-count difference was associated positively with waist circumference and inversely with midthigh circumference (table 3). Despite race-ethnic differences in the dR45 and WTR values, these associations were found for both the White and Black recruits in our Atlanta sample.

Our sample of middle-aged and older male patients from Bangalore demonstrated that those men who had larger dR45 values were less represented at older ages. Since a person's dR45 value cannot change with aging, a plausible explanation for this cross-sectional association between dR45 and age is that dR45 caused selected men to enter or leave the patient population. Men whose dR45 values were larger may have been more likely to die early. Because the relation of dR45 to age was stronger among the men with type 2 diabetes, we inferred that men with high dR45 values might have a disadvantage related to their insulin-mediated metabolism. Alternatively, the absence of older patients from a clinic population might indicate that men with high dR45 values were healthier and thus were less likely to seek medical care. Another explanation could be an unrecognized secular trend in ridge-count differences by which Bangalore men in successive birth cohorts were born with increasing dR45 values. We consider these alternative interpretations only remotely plausible, since they fail to explain why the association we found between dR45 and age applied to the diabetic patients only.

The combination of the fourth and fifth fingers of the right hand was identified empirically, and we cannot provide a complete theoretical explanation of why only dR45 defines a biologically useful variable. We presume that a large majority of men in each of our study populations was innately right-handed and that this widely noted hand preference (32Go) helps to explain why a ridge-count difference on only the right hand was associated with a pattern of fetal growth. Left-right dermatoglyphic asymmetries appear to be correlated with neurologic development (33Go), and asymmetry in fingertip ridge counts has generally been interpreted as a reflection of the gestational environment rather than of genetic endowment (34Go). In the future, it will be useful to learn whether persons who are not right-handed might provide useful gestational information related to ridge-count differences obtained from their left hands (e.g., dL45).

Our initial rationale for this study suggested that ridge-count differences between the first and fifth, rather than the fourth and fifth, fingers were more likely to represent a hypothetical cephalocaudal growth gradient in the fetus. However, we have since learned that dermatoglyphic researchers who used principal-component analysis previously identified the fourth and fifth fingers as a specific finger pair that might correspond to an underlying developmental field or gradient (35Go, 36Go). We found that dR45 was associated more strongly than dR15 with WTR (among Atlanta young men) and more strongly with reduced survivorship (among Bangalore patients). Furthermore, in our comparison of the ridge-count differences between the US White and the Indian study populations, we noted that the dR45 value tended to be larger for the Indian men but that the dR15 value did not. A previous study in which computed tomography was used found that Indian men (compared with matched Swedish men) have a smaller fraction of muscle in their legs (37Go). Therefore, our Bangalore-to-Atlanta comparison of dR45 and dR15 offers additional evidence on behalf of dR45 as a predictor of upper-body tissue distribution.

Because fingerprints are permanently fixed by week 19 of gestation, an association between any fingerprint characteristic and adult disease or mortality implies that some degree of disease risk was already established at or before the midpoint of pregnancy. Other researchers have reported that the number of fingertip whorl patterns (related to increased fingertip ridge counts) is associated with disproportionate newborn dimensions (38Go) or with adult cardiovascular conditions (39GoGo–41Go). In our anthropometric study, we found no association between whorl count and WTR (data not shown). However, our study provides evidence that dR45 might serve as a fingerprint marker of metabolic risk that originates prior to the middle of gestation. Our findings also support the notion that metabolic programming may be mediated by an influence on the relative distribution of tissue mass, perhaps specifically muscle tissue, along the cephalocaudal axis. These results do not indicate that the hazards of upper-body tissue distribution, type 2 diabetes, or insulin resistance are entirely established by the midpoint of pregnancy. More likely, throughout childhood, adolescence, and adulthood, there are many additional contributors to the accumulation of risk (42Go). If there is, however, a prenatal exposure that contributes to metabolic programming by impairing or redistributing fetal growth, then the time window for that critical exposure may occur prior to the middle of gestation.

The fingertip ridge-count difference dR45 could be a useful marker for future epidemiologic studies of the earliest influences on adult chronic disease. Whether the intrauterine environment or genes contribute to these early influences could not be determined from our data. However, fingerprints are an easily accessible, lifelong marker referable to early gestation. Thus, dR45 or other fingerprint characteristics could help to clarify how early prenatal exposures (e.g., maternal nutrition, stress hormones, toxins, oxygen transport) or heredity might be related to chronic diseases in later life. Future research should attempt to confirm that dR45 is associated with upper-body tissue distribution or metabolic programming among women as well as men and among other race-ethnic groups.


    ACKNOWLEDGMENTS
 
The authors thank the Atlanta Military Entrance Processing Station, the US Department of Defense, and the Georgia Division of Vital Records and Health Statistics for their cooperation. The participation of these organizations does not, however, imply any institutional endorsement of results or interpretations. The able assistance of Todd Pierce and Dr. David F. Williamson is also acknowledged.


    NOTES
 
Reprint requests to Dr. Henry S. Kahn, Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Highway, MS K-68, Atlanta, GA 30341 (e-mail: hsk1{at}cdc.gov).


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 DISCUSSION
 REFERENCES
 

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Received for publication April 13, 2000. Accepted for publication May 1, 2000.





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