Variability of temperature measurements recorded by a wearable device by biological sex.
Variability of temperature measurements recorded by a wearable device by biological sex.
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DOI：
10.1186/s13293-023-00558-z
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发表时间：
2023-11-01
影响因子：
7.9
通讯作者：
Smarr, Benjamin L.
中科院分区：
文献类型：
--
作者：
Bruce, Lauryn Keeler;Kasl, Patrick;Soltani, Severine;Viswanath, Varun K.;Hartogensis, Wendy;Dilchert, Stephan;Hecht, Frederick M.;Chowdhary, Anoushka;Anglo, Claudine;Pandya, Leena;Dasgupta, Subhasis;Altintas, Ilkay;Gupta, Amarnath;Mason, Ashley E.;Smarr, Benjamin L.
历史上，女性一直被排除在生物医学研究之外，部分原因是有文件证明，男性受试者的结果将有效地推广到女性。这在一定程度上是因为假设卵巢节律会增加合并随机样本的总体方差。但并不是所有样本的方差都是随机的。人类的生物特征是不断变化的刺激和生物节律的反应，单次测量零星不容易支持跨时间尺度的方差探索。最近，我们报告说，在小鼠中，纵向测量的核心体温显示男性比骑自行车的女性更高的方差，在多个时间尺度上的个体内和个体间。在这里，我们探讨纵向人体远端体温，测量的可穿戴传感器设备（Oura环），为6个月的女性和男性的年龄范围从20岁到79岁。在这项研究中，我们没有限制女性与男性的比较，而是开发了一种方法，根据夜间温度的大致每月模式的存在，将个体分类为周期性或非周期性。然后，我们使用多个标准工具比较了时间尺度上的结构和方差。正如预期的那样，性别差异存在，但在多个统计比较和时间尺度上，没有一个组的方差始终超过其他组。当跨时间的变异性进行评估，女性，无论他们的温度是否包含每月周期，没有显着差异，男性在每日和每月的时间尺度。这些发现与人类女性在月经周期中变化太大而无法纳入生物医学研究的观点相矛盾。纵向温度的女性不积累更大的测量误差随着时间的推移比男性和大多数无法解释的方差是在性别类别，而不是它们之间。女性仍然不成比例地被排除在研究之外，部分原因是有文献记载的担忧，即月经周期使她们更加多变，因此更难研究。在过去，我们曾质疑过这种说法，发现它不适用于动物生理学，动物行为或人类行为。在这里，我们能够证明，它也不适用于人类生理学。我们分析了6个月连续收集的温度数据测量的商业可穿戴设备，以确定是否是真的，女性比男性更可变或更不可预测。我们发现，温度主要是随着一天中的时间以及个体是醒着还是睡着而变化的。此外，对于一些女性，夜间最高温度包含一个周期性模式，周期约为28天，与月经周期一致。的变异性是不同的骑自行车的女性，而不是骑自行车的女性，和男性，但只有骑自行车的女性温度包含一个月的结构，使他们的变化比那些非骑自行车的女性和男性更可预测。我们发现，大多数无法解释的差异是在每个性别/周期类别，而不是他们之间。所有组在不同的时间段内都有不可区分的测量误差。这种对温度的分析表明，数据驱动的特征可能比二元性别等历史分类更有助于区分个体。这项工作还支持将女性作为研究对象纳入生物学研究，因为这一纳入不会削弱统计比较，但确实可以使世界上的研究成果得到更公平的覆盖。性别并不能区分更多和更少的变量个体，如连续的体温测量。高分辨率的分析，纵向温度在这里详细支持纳入女性在生物医学研究。骑自行车的女性在温度上的差异在几天和几个月内比男性更有结构性，但大多数差异是在群体内，而不是在群体之间。这项工作包括在没有预先存在的标签的情况下识别参与者数据中的周期的方法。
Females have been historically excluded from biomedical research due in part to the documented presumption that results with male subjects will generalize effectively to females. This has been justified in part by the assumption that ovarian rhythms will increase the overall variance of pooled random samples. But not all variance in samples is random. Human biometrics are continuously changing in response to stimuli and biological rhythms; single measurements taken sporadically do not easily support exploration of variance across time scales. Recently we reported that in mice, core body temperature measured longitudinally shows higher variance in males than cycling females, both within and across individuals at multiple time scales. Here, we explore longitudinal human distal body temperature, measured by a wearable sensor device (Oura Ring), for 6 months in females and males ranging in age from 20 to 79 years. In this study, we did not limit the comparisons to female versus male, but instead we developed a method for categorizing individuals as cyclic or acyclic depending on the presence of a roughly monthly pattern to their nightly temperature. We then compared structure and variance across time scales using multiple standard instruments. Sex differences exist as expected, but across multiple statistical comparisons and timescales, there was no one group that consistently exceeded the others in variance. When variability was assessed across time, females, whether or not their temperature contained monthly cycles, did not significantly differ from males both on daily and monthly time scales. These findings contradict the viewpoint that human females are too variable across menstrual cycles to include in biomedical research. Longitudinal temperature of females does not accumulate greater measurement error over time than do males and the majority of unexplained variance is within sex category, not between them. Women are still excluded from research disproportionately, due in part to documented concerns that menstrual cycles make them more variable and so harder to study. In the past, we have challenged this claim, finding it does not hold for animal physiology, animal behavior, or human behavior. Here we are able to show that it does not hold in human physiology either. We analyzed 6 months of continuously collected temperature data measured by a commercial wearable device, in order to determine if it is true that females are more variable or less predictable than males. We found that temperatures mostly vary as a function of time of day and whether the individual was awake or asleep. Additionally, for some females, nightly maximum temperature contained a cyclical pattern with a period of around 28 days, consistent with menstrual cycles. The variability was different between cycling females, not cycling females, and males, but only cycling female temperature contained a monthly structure, making their changes more predictable than those of non-cycling females and males. We found the majority of unexplained variance to be within each sex/cycling category, not between them. All groups had indistinguishable measurement errors across time. This analysis of temperature suggests data-driven characteristics might be more helpful distinguishing individuals than historical categories such as binary sex. The work also supports the inclusion of females as subjects within biological research, as this inclusion does not weaken statistical comparisons, but does allow more equitable coverage of research results in the world. Sex does not separate more and less variable individuals, as measured in continuous body temperature. Analysis of high-resolution, longitudinal temperature detailed here supports the inclusion of females in biomedical research. Cycling female variance in temperature is more structured across days and months than males, but most variance is within groups, and not between them. This work includes methods for identifying cycles in participant data without pre-existing labels.
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影响因子：
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