8. Discusión
8.2 Ensayos con poblaciones de Arthrospira platensis
Some variables to be used in statistical analyses were created as part of data management pro- cesses to convert questionnaire responses into formats meaningful for some analyses. Most of them are standard calculations, such as Body Mass Index (BMI) or age at first birth, or common transformations such as converting continuous variables to categorical for some analyses. How- ever variables of socio–economic status and milk consumption are less obvious and require further explanation.
Measurements of socio–economic status are highly dependent on the particularities of a given group. While income or expenditure are straightforward and desirable continuous measures in many cases, these methods cannot account for situations where the assumption of a completely monetary economy is not met, and thus are less useful in context where non–monetary means of subsistence (such as autonomous food production) are prevalent, or where wage labour is sporadic and informal (Falkingham & Namazie, 2002). A common approach for these cases is the use of information about access to certain household goods or conveniences, generally coded as binary variables (presence/absence), to build scores termed asset indexes (Booysen et al., 2008)10.
To construct asset indexes, all variables are processed using multidimensional scaling techniques, to get a factorial weight based on the contribution to the inertia at the first principal axis of each assessed household good. Although most studies use Principal Components Analysis, the categor- ical nature of the original data dictates Multiple Correspondence Analysis as more appropriate, as has been demonstrated comparing differences in the results according to the selected technique (Asselin & Anh, 2008; Booysen et al., 2008).
In this thesis, 10 binary variables11 were used to construct a wealth score based on standardised
10Terms such as “score”, “index”, “composite score”, or ‘‘score index” are used interchangeably in the mentioned literature. 11The variables were whether the participant or his/her household have/doesn’t have or have access to: tap water,
2.3. Data analysis methods 81 factorial weights obtained from the contribution of each to the total inertia on the first axis (83.14% of the variance) in a Multiple Correspondence Analysis.
Estimation of levels of consumption of milk and milk products presents similar difficulties regarding particular lifestyles in different societies and difficult techniques for data collection. Methods frequently used according to the literature can be classified in three broad categories: dietary recalls, food frequency questionnaires, and diet journals (Thompson & Subar, 2008).
In recalling methods, the participant is asked to mention what he/she ate in a given period of time, or asked to recall amounts of consumption of particular foods in that period. As long as the period is recent and short, this methods has the advantage of collecting a more accurate picture of actual consumption rather than self–perception. However, it fails to capture seasonality in levels of consumption, and results will be distorted if the person had an exceptional dietary behaviour during the recalled period (Dehghan et al., 2005).
Food frequency questionnaires are based on rating scale responses to questions formulated as how regularly and how much a person consumes a given food, either in general terms or referring to specific long and distant periods of time (e.g. last months, a given year, when unemployed, during drought, etc.). Unlike recall methods, questionnaires are even more heavily influenced by the image a participant wants to project, self–perception of diet, and other psychological factors (Kumanyika, 2003; Marks et al., 2006), but they have the advantage of being able to refer to specific periods of interest and are less influenced by exceptional behaviour at the time of the survey. Journals are based on periodical records (at least once a day) of every meal a subject has had, indicating quantities in detail (sometimes weighting the food). As long as they are completed for enough time, journals can capture variability due to different situational factors, and account for exceptions while recording actual food consumption. They can be done either by researchers or by the participants themselves, with implications in both invasiveness and accuracy respectively. The obvious drawback is the intensive workload demanded by this method (Kumanyika, 2003).12
The questionnaires used during the pilot study included four questions about consumption of fresh milk and milk products; frequencies and quantities of consumption were assessed using both re- call and questionnaire frequency methods, by asking for a specific dietary recall of last week’s consumption, and general consumption through the year. The question of general dietary beha- viour was posed first, asking for quantities and general frequency of milk and dairy products. Then, we asked for a dietary recall for the last seven days, asking how many days a week milk and milk products were consumed. Most participants expressed their concern about the effect of seasonality
electricity, ceiling, floor, water heater, washing machine, fridge, television, computer and vehicle.
2.3. Data analysis methods 82 in their responses, arguing that milk consumption was higher between September and February, when goats were milking.
The experience during the pilot suggested some confusion in the participants caused by the way these questions were asked, and the results, though there was correlation between employed methods, were suspiciously unexpected. These problems led us to change the way data on milk consumption was collected during the main fieldwork.
One of the problematic aspects of the first set of questions was to ask separately for both frequen- cies and quantities of milk consumption, which was confusing for the participants and difficult to analyse afterwards. To solve this problem, both questions on frequencies and quantities were fused into a single scale adapted from the Adults Food Frequency Questionnaire developed by the Department of Nutrition at the University of Harvard (Harvard School of Public Health, 2007). Questions regarding seasonality of consumption were added as suggested by participants. To improve dietary recalls, we added a question focused on quantities of consumption the day before and kept the questions based on last week for comparative purposes. Surprisingly, the results were in agreement with those obtained on the pilot study and no significant differences were found on reported seasonal consumption (see section 3.3.2).
For the pilot phase, estimation of cups/portions a day were calculated multiplying responses on frequency and quantity, and therefore introducing a large amount of error to the final estimation. This was changed during the main fieldwork, and participants chose one option from a scale of different portions/cups of milk/milk products on a specific frequency (i.e. never, two cups a week, one cup a day, etc.), allowing us to estimate number of cups/portion a day directly from their response.
2.3.4.2 Statistical models
Several models were used to find associations between variables related with lactase persistence and variables related with selective advantages, tested using analysis of variance (ANOVA), analysis of covariance (ANCOVA), linear regression, and generalised linear models.
In general, they were built to evaluate the effect of inferred lactase persistence status on height, weight, fertility or child mortality, as response variables. Other variables, such as sex, age, age at first birth, birth cohort, socio-economic status, ancestry, inbreeding, and matrices of population structure were included in models as confounding variables. A summary of the general scheme of these models is shown in Table 2.6.
2.3. Data analysis methods 83
Input/explanatory Output/response Confounding variable/covari- ant/fixed or mixed effects
Lactase persistence status Height Sex
Weight Age
BMI Age at first birth
Total No. of children Socio-economic status Surviving children Ancestry
Child mortality Inbreeding
Matrices PSA/STRUCTURE (see 2.3.3.2)
Table 2.6.Usage of variables according to the general scheme of most models tested in this thesis.
84
Chapter 3
General Results
A
descriptive overview of the results of this study is presented in this Chapter, in- cluding the first report to our knowledge of the frequencies of lactase persistence in a pastoralist population of the New World, and the study of the association between the European variant of lactase persistence and lactose digestion phenotype in one of these populations. Additionally, the chapter provides an overview of the demographic profile, milk consumption, and height and weight in the sample.3.1. Genotype – Phenotype association 85
3.1
Association of −13,910*T and phenotyped lactase persistence
As previously mentioned, −13,910*T is highly associated with and causative of lactase persistence in European and Indian populations (Enattah et al., 2002; Gallego Romero et al., 2012), but not in some other populations, where other genetic variants have been found (Enattah et al., 2008; Gallego Romero et al., 2012; Imtiaz et al., 2007; Ingram et al., 2007; Jones et al., 2013; Ranciaro et al., 2014; Tishkoff et al., 2007). Because of the historical background of these Coquimbo popula- tions, introduction of the −13,910*T through Spanish ancestry is likely, but introduction of variants originating from other continents is less likely but also possible.
To confirm this, and to test for the presence of any previously reported or de novo enhancer muta- tions, a total of 41 volunteers were phenotyped by lactose tolerance test (see Section 2.2.8). Of these, lactose digestion status could not be determined for 3 participants due to fluctuating rise in breath hydrogen but without a substantial rise after 3 hours1. In one participant, starting breath
hydrogen was above 20 ppm, suggesting that fasting was not accomplished, and in another par- ticipant starting breath hydrogen was 0 ppm and remained so all through the 3 hours of testing, suggesting no production of hydrogen.
LTT phenotype CC CT TT Total Non–digesters 17 0 0 17 Digesters 1 2 16 19 Indeterminate 3 1 0 4 H2non-producer 1 0 0 1 Total 22 3 16 41
Table 3.1.Association of lactose tolerance test phenotypes and LCT −13,910 genotype in 99.64% of the subjects (Fisher’s exact test p-value = 4.187 × 10−9). No other lactase persistence variants were found.
Of the remaining 36 acceptable lactose tolerance tests, 19 participants were classified as lactose digesters, and 17 as non–digesters. Complete sequencing of the enhancer region showed that 18 digesters carried −13,910*T, while none of the non–digesters did. No other lactase persistence variants were found. Results of this lactose tolerance test were highly associated with predicted status according to −13,910 genotype (Fisher’s exact test p-value = 4.187 × 10−9see table 3.1).
The one participant genotyped as −13,910 C/C and as a digester according to lactose tolerance test, showed no additional variants in the enhancer sequence2.
1Time consumption of the test was an important limitation in the recruitment of volunteers and the achieved sample
size.
2Some speculative ideas to explain the results of this C/C digester could be a very slow digestion, insufficient air flow
3.1. Genotype – Phenotype association 86 These results are in agreement with published studies for South America (Bulhões et al., 2007; Morales et al., 2011) and allow us to suppose that the principal allelic variant causative of lactase persistence in this population is −13,910*T, and thus to consider carriers of this variant as lactase– persistent and non–carriers likely non–persistent.