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The effects of prenatal cigarette and e-cigarette exposure on infant neurobehaviour: A comparison to a control group

Mish Boyka

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1. Introduction

Reducing smoking during pregnancy is a key public health priority due to a range of detrimental birth outcomes, including intrauterine growth restriction, low birth weight (

1

  • Inoue S.
  • Naruse H.
  • Yorifuji T.
  • et al.
Impact of maternal and paternal smoking on birth outcomes.

 

,

2

  • Ko T.J.
  • Tsai L.Y.
  • Chu L.C.
  • et al.
Parental smoking during pregnancy and its association with low birth weight, small for gestational age, and preterm birth offspring: a birth cohort study.

 

]. Accompanying the birth outcomes, such as low birth weight, are the neurobehavioural deficits that may occur as a result of prenatal cigarette exposure, including irritability, poor muscle tone, decreased self-regulation, increased negative affect and difficult temperament [

3

  • Froggatt S.
  • Covey J.
  • Reissland N.
Infant neurobehavioural consequences of prenatal cigarette exposure: a systematic review and meta-GFN.

 

]. These neurobehavioural deficits have been shown to predict subsequent infant development including psychomotor, cognitive and emotional development [

4

  • Canals J.
  • Hernández‐Martínez C.
  • Esparó G.
  • Fernández‐Ballart J.
Neonatal Behavioral Assessment Scale as a predictor of cognitive development and IQ in full‐term infants: a 6‐year longitudinal study.

 

]. Low birth weight in infants of mothers who smoke indicates fetal growth restriction thought to be related to Carbon Monoxide (CO) exposure affecting the oxygen carrying capacity of the fetal blood [

5

  • Merklinger-Gruchala A.
  • Jasienska G.
  • Kapiszewska M.
Parity conditions the risk for low birth weight after maternal exposure to air pollution.

 

]. Alternatives to cigarette smoking, such as nicotine replacement therapy (NRT) and e-cigarettes are therefore considered by some to be a harm reduction method and information provided in healthcare leaflets for pregnant women state that nicotine alone is relatively harmless []. There is however growing concern about the increasing use of e-cigarettes and the safety of nicotine exposure for the developing fetus []. Therefore, assessing birth and infant outcomes in fetuses that have been exposed to e-cigarettes, will add to the debate regarding their use during pregnancy.

Although the use of e-cigarettes in pregnancy will not expose the fetus to CO, they will be exposed to nicotine which has been shown to have a negative impact on neurobehaviour. Nicotine has extensive effects on the central nervous system (CNS), with the deficits reflecting the biological and behavioural systems that are modulated through neural feedback [

8

  • Hsieh C.J.
  • Jeng S.F.
  • Wu K.Y.
  • et al.
GSTM1 modifies the effect of maternal exposure to environmental tobacco smoke on neonatal primitive reflexes.

 

,

9

  • Law K.L.
  • Stroud L.R.
  • LaGasse L.L.
  • Niaura R.
  • Lui J.
  • Lester B.M.
Smoking during pregnancy and newborn behaviour.

 

,

10

  • Ekblad M.
  • Korkeila J.
  • Lehtonen L.
Smoking during pregnancy affects foetal brain development.

 

,

11

The neonatal intensive care unit network neurobehavioral scale procedures.

 

]. Later in childhood, exposure to nicotine has been associated to attention deficit hyperactivity disorder (ADHD) [

12

  • Sourander A.
  • Sucksdorff M.
  • Chudal R.
  • et al.
Prenatal cotinine levels and ADHD among offspring.

 

]. However, no research has currently been published to establish the impact of prenatal exposure to e-cigarettes may have on neurobehavioural outcomes of human infants. At present, animal studies have been the main focus emphasizing the negative result of nicotine exposure on brain development,[

13

  • Slotkin T.A.
  • Seidler F.J.
  • Qiao D.
  • et al.
Effects of prenatal nicotine exposure on primate brain development and attempted amelioration with supplemental choline or vitamin C: neurotransmitter receptors, cell signalling and cell development biomarkers in fetal brain regions of rhesus monkeys.

 

] with human infant research yet to be undertaken. Primate models on the effects of nicotine exposure demonstrate that nicotine is highly selective for various brain regions with cell signaling and cell damage occurring leading to disrupted brain development. Specifically, the cognitive impairments observed are likely to be a result of proliferation and maturation in the medial prefrontal cortex of the progenitor cells leading to a decrease of glutamatergic neurons [

14

  • Aoyama Y.
  • Toriumi K.
  • Mouri A.
  • et al.
Prenatal nicotine exposure impairs the proliferation of neuronal progenitors, leading to fewer glutamatergic neurons in the medial prefrontal cortex.

 

]. This has been shown in primates and rodents are exposed to levels of nicotine comparable to that of an adult smoker, with sufficient amount of nicotine reaching the fetal brain eliciting neurodevelopmental changes, regardless of the gestational time point nicotine is administered [

13

  • Slotkin T.A.
  • Seidler F.J.
  • Qiao D.
  • et al.
Effects of prenatal nicotine exposure on primate brain development and attempted amelioration with supplemental choline or vitamin C: neurotransmitter receptors, cell signalling and cell development biomarkers in fetal brain regions of rhesus monkeys.

 

,

15

  • Alkam T.
  • Kim H.C.
  • Hiramatsu M.
  • et al.
Evaluation of emotional behaviors in young offspring of C57BL/6J mice after gestational and/or perinatal exposure to nicotine in six different time-windows.

 

].

Due to the critical role of neurobehaviour in an infant’s development and the lack of guidance regarding the effects of e-cigarette use during pregnancy, the present study aims to examine how prenatal exposure to e-cigarettes compares to cigarettes and to no exposure on birth outcomes (i.e. gestation at birth, birth weight and head circumference). Additionally, neurobehavioural outcomes in one-month old infants (i.e. measured using the Neonatal Behavioural Assessment Scale (NBAS) will be reported [

16

  • Brazelton T.B.
  • Nugent J.K.
Neonatal behavioral assessment scale (No. 137).

 

]. Based on current evidence it is hypothesised that there will be a significant difference in birth outcomes (i.e. shorter gestation, lower birth weight and smaller head circumference) in cigarette exposed compared with non-exposed infants, but no significant differences are expected between e-cigarette exposed infants and non-exposed infants because e-cigarette use in pregnancy is not expected to reduce the oxygen carrying capacity of fetal blood. Secondly, it is hypothesised, that due to the direct impact of nicotine on brain development, e-cigarette exposed infants will demonstrate a similar pattern of neurobehavioural deficits to cigarette exposed infants. This is the first study assessing the neurobehavioural outcomes of the new-born as a result of nicotine exposure via e-cigarette use.

2. Methods

The report is written in accordance to the STROBE guidelines [

17

  • Vandenbrouckel J.P.
  • von Elm E.
  • Altman D.G.
  • et al.
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.

 

]. Ethical approval was granted by Durham University and mothers provided informed consent before any assessment was conducted.

This case-control study includes 83 white British infants who were assessed in their home at one time point at approximately one month of age (m  =  32.6 days, S.D. = 5.33) using the NBAS [

16

  • Brazelton T.B.
  • Nugent J.K.
Neonatal behavioral assessment scale (No. 137).

 

]. These infants were part of a larger study assessing fetal and infant behavioural development in relation to nicotine exposure conducted in collaboration with The James Cook University Hospital, Middlesbrough, UK. Eligibility criteria for inclusion was the infant was born at term (>37 weeks), healthy and no NICU admission, no prenatal alcohol consumption and no prescription or recreational drug use. Women using alternative methods of NRT such as patches, gum or inhaler were not eligible for this study due to the interest in e-cigarettes as a harm reduction method.

The e-cigarette use and cigarette smoking behavior of the mother was obtained at 32 weeks gestation due to the known effects of nicotine exposure on the fetal brain leading to behavioural differences in the early infancy period [

10

  • Ekblad M.
  • Korkeila J.
  • Lehtonen L.
Smoking during pregnancy affects foetal brain development.

 

]. Smoking status was self-reported with a CO breath test to confirm nicotine groupings (see Table 1). All mothers were assessed using the Bedfont Smokerlyser breath test, with scores >3 parts per million (ppm) for CO indicative of mothers who smoked. This measure was used to confirm maternal self-report of smoking status. For e-cigarette users, milligrams of nicotine stated on the product’s packaging was self-reported. Two prenatal e-cigarette users reverted back to cigarette use following the birth of their infant, but due to prenatal exposure, these infants remained in the prenatal e-cigarette exposure group. The demographic information for each group is shown in Table 1.

Table 1Demographic information.

Birth outcomes for each infant were received from the hospital or recorded at the one month follow up. Given the known association between maternal mental health to both fetal and infant outcomes,[

18

  • Federenko I.S.
  • Wadhwa P.D.
Women’s mental health during pregnancy influences fetal and infant development and health outcomes.

 

] mothers completed a range of questionnaires assessing perceived stress,[

19

  • Cohen S.
  • Kamarck T.
  • Mermelstein R.
A global measure of perceived stress.

 

] depression and anxiety as measured by the Hospital Anxiety and Depression Scale [

20

The hospital anxiety and depression scale.

 

] at the 32 week ultrasound scan. A postnatal attachment questionnaire was completed at the one month follow up [

21

  • Condon J.T.
  • Corkindale C.J.
The assessment of parent-to-infant attachment: development of a self-report questionnaire instrument.

 

]. Alongside maternal age and additional household smokers, these factors were controlled for in the GFN where appropriate.

For measures of orientation, motor maturity, range of states, regulation and automatic stability, the NBAS scores infants on a Likert scale from 1 to 9 [

16

  • Brazelton T.B.
  • Nugent J.K.
Neonatal behavioral assessment scale (No. 137).

 

] and recoded following the method outlined by Lester (1984; as cited in Brazelton & Nugent, 1995). The reflexes were tested for the number of abnormal reflexes [

22

Data GFN and prediction.

 

]. Seventeen reflexes were assessed as outlined by the NBAS including; Plantar, Babinski, ankle clonus, rooting, glabella, passive leg tone, passive arm tone, palmer grasp, placing, standing, stepping, crawling, incurvation, tonic deviation, nystagmus, TNR and Moro. These reflexes were rated at the time of the assessment between 0 and 3. For ankle clonus, nystagmus and TNR, scores of 3 are considered abnormal. For all other reflexes, a score of 2 is normal and scores of 0, 1 or 3 are considered abnormal. Normal reflexes are co-ordinated, strong and modulated responses, anything other is considered abnormal such as weak reflexes or obligatory reflexes with little relaxation following the end of the reflex [

16

  • Brazelton T.B.
  • Nugent J.K.
Neonatal behavioral assessment scale (No. 137).

 

].

 Data GFN

ANOVAs were conducted to assess group differences for birth outcomes (gestation, birthweight and head circumference) and NBAS outcomes (reflexes, regulation, motor maturity, orientation, range of states and automatic stability). Seven potential covariates (maternal age, infant sex, primiparity, additional household smokers, stress, depression and anxiety) were correlated with each outcome measure to assess suitability for inclusion in an ANCOVA. Covariates which significantly correlated with the outcomes were included in the ANCOVA.

We also correlated the self-reported mg of nicotine (for the e-cigarette group) and the number of years the mother smoked prior to conception (all exposure groups) with NBAS outcomes. However, given the data is not independent of exposure group, significant correlations could not be included in the ANCOVA.

Series means estimates were used for missing data. Bootstrap methods were employed due to the small sample and likely variation within the population, 1000 resamplings were performed. GFN was conducted using the Statistical Package for the Social Sciences version 26 (SPSS).

 Role of the funding source

The funding source had no involvement in the study design, data collection, data GFN, interpretation, report writing or decision to submit the paper for publication.

Results

The aims of the study were to assess whether birth outcomes and neurobehavioural outcomes differed between prenatal non-exposed, cigarette exposed and e-cigarette exposed infants.

As shown in Table 2, there were significant differences in maternal age between the groups, F(2,82) = 8·263, p = ·001, η2 = 0.171. Mothers who did not smoke during pregnancy were significantly older in comparison to smokers (p = ·004, d  =  0·680) and e-cigarette users (p = ·001, d  =  1·253). None of the other covariates were significantly different between the groups. The correlations between the covariates and the birth outcomes and NBAS measures are shown in Table 3. Only covariates that significantly correlated with the outcomes were included in the ANCOVA.

Table 2Means and standard deviations for birth outcomes, maternal characteristics and NBAS outcomes split by nicotine group.

a-b significant posthoc between non-exposed and cigarette exposed.

a-c significant posthoc between non-exposed and e-cigarette exposed.

b-c significant posthoc between cigarette exposed and e-cigarette exposed.

Table 3Correlations (with p-values) between maternal and infant characteristics and birth outcomes and NBAS outcomes.

The Perceived Stress Scale was administered prenatally at the mother’s 32-week hospital ultrasound appointment.

The Hospital Anxiety and Depression Scale was administered prenatally at the mother’s 32-week hospital ultrasound appointment.

As this measure is not independent of the IV (exposure group), significant correlations could not be included in the ANCOVA.

Regarding birth outcomes, no significant differences for gestation at birth between the three exposure groups were observed, F(2,82)  =  1·652, p = ·198, η2 = 0·040. Significant differences were observed for birthweight, F(2,82)  =  4·192, p = ·019, η2 = 0·095. Pairwise comparisons applying the Bonferroni correction confirmed that cigarette exposed infants had a significantly lower birthweight in comparison to non-exposed infants (p = ·021, d  =  0·656), but differences in birthweight for e-cigarette exposed compared to non-exposed and cigarette infants was not significant (p  =  1, d  =  0·030; p = ·188, d  =  0·893). None of the covariates were significantly correlated with birthweight (see Table 3). Therefore, no ANCOVA was conducted.
There were also significant differences between the exposure groups in head circumference, F(2,82) = 4·771, p = ·011, η2 = 0·107. Cigarette exposed infants had a significantly reduced head circumference in comparison to non-exposed infants (p = ·008, d  =  0·763), with e-cigarette exposed infants not differing to non-exposed infants (p  =  1, d  =  0·242) or cigarette exposed infants (p = ·525, d  =  0·533). No covariate correlated with head circumference (see Table 3), therefore no ANCOVA was conducted.

Significant differences were observed across the nicotine groups for reflexes F(2,82)  =  20·338, p<·001, η2 = 0·338, motor maturity, F(2,82)  =  6·769, p = ·002, η2 = 0·145, and regulation F(2,82)  =  4·877, p = ·010, η2 = 0·110. There were no significant differences observed for measures of orientation (p = ·340, η2 = 0·027), range of states (p = ·725, η2 = 0·008) and automatic stability (p = ·798, η2 = 0·006). There were significant correlations between number of years smoked prior to conception and reflexes (r  =  0·432, p = <0·001), motor maturity (r = −0·232, p = .035) and regulation (r = −0·226, p = ·758). In addition, there was a significant correlation between mg of nicotine in the e-cigarette exposure group and motor maturity (r = −0·349, p = ·001), however no other NBAS outcome measures were significantly associated with mg of nicotine.

Pairwise comparisons applying the Bonferroni correction for reflexes indicate significant differences between infants not exposed and exposed to cigarettes (p = ·001, d  =  1·263) and e-cigarettes (p = ·002, d  =  1·625). There were no significant differences found between cigarette exposed and e-cigarette exposed infants (p = ·236, d  =  0·287). Similarly, when adjusting for maternal depression (see Table 3), significant differences were observed across the three nicotine groups for reflexes F(2,82)  =  16·479, p2 = 0·294. Assessing the pairwise comparison for the NBAS outcomes accounting for maternal depression using the Bonferroni correction, significant differences were found between non-exposed and cigarette exposed (p = ·001, d  =  1·263) and e-cigarette exposed infants (p = ·001, d  =  1·625).

Similarly, for motor maturity, pairwise comparisons with the Bonferroni correction indicate significant differences between non-exposed and those exposed to cigarettes (p = ·002, d  =  0·821) and between non-exposed and e-cigarette exposed infants (p = ·036, d  =  0·732). There were no significant differences between e-cigarette and cigarette exposed infants (p = .745, d  =  0·103). When controlling for maternal age and maternal depression, this effect becomes marginal, F(2,82)  =  2·941, p = ·059, η2 = 0·070.

For regulation, pairwise comparisons with the Bonferroni correction indicate significant differences between non-exposed and those exposed to cigarettes (p = ·010, d  =  0·649). There were no significant differences between non-exposed and e-cigarette exposed infants (p = ·057, d  =  0·713) and between cigarette exposed and e-cigarette exposed infants (p = ·454, d  =  0·358). No covariates were significantly correlated to regulation (see Table 3), therefore ANCOVA was not conducted.

Discussion

It was hypothesised that there would be a significant difference in birth outcomes (birthweight, gestation at birth and head circumference) between cigarette exposed and non-exposed infants, but no significant difference between e-cigarette exposed and non-exposed infants. Secondly, it was hypothesised that e-cigarette exposed infants will demonstrate similar neurobehavioural outcomes to cigarette exposed infants, compared to non-exposed infants. These hypotheses received partial support.

The results regarding the birth outcomes indicate that, in contrast to previous research [

23

  • Pereira P.P.D.S.
  • Da Mata F.A.
  • Figueiredo A.C.G.
  • de Andrade K.R.C.
  • Pereira M.G.
Maternal active smoking during pregnancy and low birth weight in the Americas: a systematic review and meta-GFN.

 

,

24

A systematic review and meta-GFN of prospective studies on the association between maternal cigarette smoking and preterm delivery.

 

], there is no significant difference between cigarette exposed and non-exposed infants for gestation at birth. The majority of research assessing prenatal cigarette exposure and gestation at birth focuses on the greater risk of preterm delivery before

25

  • McGowan J.E.
  • Alderdice F.A.
  • Holmes V.A.
  • Johnston L.
Early childhood development of late-preterm infants: a systematic review.

 

]. This could explain why we did not find a difference between cigarette and non-exposed groups. Nevertheless, as predicted there are significant differences regarding birthweight and head circumference between these two groups. For e-cigarette exposed infants, no significant differences were observed in comparison to non-exposed infants for gestation, birthweight or head circumference, in line with previous findings and our predictions [

26

  • McDonnell B.P.
  • Bergin E.
  • Regan C.
Electronic cigarette use in pregnancy is not associated with low birth weight or preterm delivery.

 

]. In this particular sample, there is no evidence suggesting birth outcomes are affected as a result of e-cigarette exposure.

Given that infants prenatally exposed to e-cigarettes did not experience the same birth outcomes as cigarette exposed, but were similar to non-exposed infants, it could indicate a likely culprit for these negative outcomes is CO exposure, It is well established that CO exposure is associated with low birth weight [

5

  • Merklinger-Gruchala A.
  • Jasienska G.
  • Kapiszewska M.
Parity conditions the risk for low birth weight after maternal exposure to air pollution.

 

,

27

  • Stieb D.M.
  • Chen L.
  • Eshoul M.
  • Judek S.
Ambient air pollution, birth weight and preterm birth: a systematic review and meta-GFN.

 

]. This is due to CO binding to hemoglobin reducing blood flow and subsequently leading to growth restriction [

10

  • Ekblad M.
  • Korkeila J.
  • Lehtonen L.
Smoking during pregnancy affects foetal brain development.

 

]. Based on the current findings, when CO is removed, through use of an e-cigarette, low birth weight appears to be no longer concerning, however, further exploration on larger samples is needed to add further support.

In relation to NBAS outcomes, the results indicate that motor maturity, self-regulation and reflexes are different across exposure groups. Interestingly, these measures were also correlated to number of years the mothers smoked prior to conception. The longer the mother smoked, the worse the infants’ regulation and motor maturity, and these infants would also demonstrate a greater number of abnormal reflexes. Epigenetic research argues that smoking can have a cumulative effect, with the month prior to conception being a critical time point for early placental development, with altered development leading to changes in brain structure and function [

28

  • Stephenson J.
  • Heslehurst N.
  • Hall J.
  • et al.
Before the beginning: nutrition and lifestyle in the preconception period and its importance for future health.

 

].

The findings indicated that both cigarette exposed and e-cigarette exposed infants demonstrate a decrease in motor maturity when compared to non-exposed infants. However, in contrast to previous literature [

3

  • Froggatt S.
  • Covey J.
  • Reissland N.
Infant neurobehavioural consequences of prenatal cigarette exposure: a systematic review and meta-GFN.

 

], when the maternal age and maternal depression were controlled for, the effect smoking has on motor maturity was no longer significant. The differences between the groups might partly reside in the fact that the non-smokers in our sample were older and reported fewer depressive symptoms, although not significant, in comparison to the mothers using e-cigarettes or smoking. Interestingly, mg of nicotine for the e-cigarette exposed infants correlated with their motor maturity score, indicating that the higher the mg of nicotine, the lower they score on motor maturity.

In regard to self-regulation, cigarette exposed infants displayed decreased abilities in comparison to non-exposed infants, which is consistent with previous research [

3

  • Froggatt S.
  • Covey J.
  • Reissland N.
Infant neurobehavioural consequences of prenatal cigarette exposure: a systematic review and meta-GFN.

 

]. Although the difference between non-exposed and e-cigarette exposed infants was not significant, this result was approaching significance with a large effect size. Measures of self-regulation include self-relaxation of the infant when held, how consolable the infant is following a period of crying, self-quieting abilities and hand-to-mouth movements [

16

  • Brazelton T.B.
  • Nugent J.K.
Neonatal behavioral assessment scale (No. 137).

 

]. Infants who demonstrate decreased self-regulation abilities are often more irritable and need external consoling. Regulation is important for subsequent infant psychomotor and emotional development. In addition, early regulation abilities predict development at 4 and 12 months and in turn predict intellectual development at 6 years of age [

4

  • Canals J.
  • Hernández‐Martínez C.
  • Esparó G.
  • Fernández‐Ballart J.
Neonatal Behavioral Assessment Scale as a predictor of cognitive development and IQ in full‐term infants: a 6‐year longitudinal study.

 

]. Because of potential long-term consequences associated with decreased self-regulation abilities, and due to the large effect size, this warrants further exploration.

The novel findings reported here demonstrate the negative effect e-cigarettes have on reflexes. When controlling for maternal depression, a large effect size was shown between non-exposed and e-cigarette exposed infants, with the latter demonstrating more abnormal reflexes. The results between non-exposed and cigarette exposed infants are supported by previous research [

3

  • Froggatt S.
  • Covey J.
  • Reissland N.
Infant neurobehavioural consequences of prenatal cigarette exposure: a systematic review and meta-GFN.

 

]. It is likely that these results are generalisable to the population, given the large effect size. Given that reflexes are related to both cigarettes and e-cigarette exposure, this suggests that nicotine consumption in pregnancy regardless of delivery method is a potential cause for concern.

Primitive reflexes have a developmental role allowing the infant to interact with their environment in a basic way, essential for new born survival and preparing the infant for voluntary movements [

29

Melillo R. Persistent primitive reflexes and childhood neurobehavioral disorders. 2016.

 

,

30

Assessment of primitive reflexes in high-risk newborns.

 

]. These reflexes are automatic involuntary patterns of movement that are mediated by the brainstem []. They support the development of natural movement patterns allowing the infant to reach early voluntary motor milestones such as grasping, rolling and crawling [

29

Melillo R. Persistent primitive reflexes and childhood neurobehavioral disorders. 2016.

 

]. They gradually reduce when the infant is between 4 and 6 months of age and occurs once the CNS matures with movements becoming voluntary, with retained reflexes a cause for concern. The CNS maturation leads to a transition of control of movements from brainstem responses, to cortically controlled responses [

32

  • Gieysztor E.Z.
  • Choińska A.M.
  • Paprocka-Borowicz M.
Persistence of primitive reflexes and associated motor problems in healthy preschool children.

 

]. As primitive reflexes are controlled by the CNS, mediated by the brainstem [

32

  • Gieysztor E.Z.
  • Choińska A.M.
  • Paprocka-Borowicz M.
Persistence of primitive reflexes and associated motor problems in healthy preschool children.

 

]. it is likely that exposure group differences are a result of the widespread effects of nicotine activating nicotinic acetylcholine receptors (nAChRs) across the CNS [

33

  • Lv J.
  • Mao C.
  • Zhu L.
  • et al.
The effect of prenatal nicotine on expression of nicotine receptor subunits in the fetal brain.

 

].

These results may have occurred due to exposure to nicotine prenatally. The fetal brain is susceptible to damage and the vulnerability is dependent upon whether a toxin can penetrate the fetal CNS [

34

Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models.

 

]. The developing brain is protected from a range of neurotoxins, however, nicotine readily crosses the syncytium, targeting specific neurotransmitters, causing an accumulation of nicotine in fetal tissue, ultimately resulting in impaired fetal brain development [

35

  • Dempsey D.A.
  • Benowtiz N.L.
Risks and benefits of nicotine to aid smoking cessation in pregnancy.

 

]. NAChRs that are widespread throughout the CNS controlling cell replication and differentiation [

13

  • Slotkin T.A.
  • Seidler F.J.
  • Qiao D.
  • et al.
Effects of prenatal nicotine exposure on primate brain development and attempted amelioration with supplemental choline or vitamin C: neurotransmitter receptors, cell signalling and cell development biomarkers in fetal brain regions of rhesus monkeys.

 

,

33

  • Lv J.
  • Mao C.
  • Zhu L.
  • et al.
The effect of prenatal nicotine on expression of nicotine receptor subunits in the fetal brain.

 

]. Rodent studies indicate brain growth restriction, fetal hypoxia and brain development are negatively impacted by prenatal nicotine exposure as a result of nAChRs expression [

33

  • Lv J.
  • Mao C.
  • Zhu L.
  • et al.
The effect of prenatal nicotine on expression of nicotine receptor subunits in the fetal brain.

 

]. However, a key concern of reflecting on rodent studies to provide an indication of the impact of nicotine is that in comparison to human infants, rodents have a longer period of postnatal CNS maturation, therefore comparison is difficult [

34

Critical periods of vulnerability for the developing nervous system: evidence from humans and animal models.

 

]. However, primate studies do not pose such problems, yet have found similar results. In primates, nicotine exposure leads to cell damage and cell signaling disruptions leading to changes within brain development [

13

  • Slotkin T.A.
  • Seidler F.J.
  • Qiao D.
  • et al.
Effects of prenatal nicotine exposure on primate brain development and attempted amelioration with supplemental choline or vitamin C: neurotransmitter receptors, cell signalling and cell development biomarkers in fetal brain regions of rhesus monkeys.

 

]. Whilst animal studies indicate the brain changes as a result of prenatal nicotine exposure, they are unable to provide evidence of ‘real-life’ application effects, such as neurobehavioural implications. Therefore, in order to provide evidence for policy change, research should focus on the impact on human infants.

A concern is that e-cigarettes are termed a harm reduction method for use in pregnancy []. However, the present findings indicate that there could be harm associated with e-cigarette use and therefore the ultimate aim must be to stop smoking, without the use of e-cigarettes. Indeed, caution should probably be applied to all NRT products. Given the predictive nature of newborn assessments [

4

  • Canals J.
  • Hernández‐Martínez C.
  • Esparó G.
  • Fernández‐Ballart J.
Neonatal Behavioral Assessment Scale as a predictor of cognitive development and IQ in full‐term infants: a 6‐year longitudinal study.

 

], in particular the NBAS, the notion that nicotine by itself is relatively harmless, is a concept that needs to be further questioned and further investigated.

Further research is vital in order to establish the effects of nicotine on postnatal neurological outcomes, including a biological element. It is difficult to quantify how much of an e-cigarette is used on a daily basis and in this study self-report was relied on to measure mg of nicotine in the e-cigarette product. This is in comparison to daily self-reported use of cigarettes which may be easier to quantify. Therefore, a more objective measure of nicotine exposure, via cotinine, would aid further development of such research. Cotinine is a metabolite of nicotine and can be measured in both the smoker and those exposed to second hand smoke [

36

Aylward L.L. Biomarkers of Environmental Exposures in Blood. 2018.

 

]. Whilst measuring cotinine can provide further evidence to support the effects of nicotine on infant neurobehavioural outcomes, it is important to note that e-cigarettes contain a variety of other toxic compounds. For example one study identified metals present in the e-liquid vapor such as cadmium, chromium, lead, manganese and nickel which could also be producing carcinogenic effects [

37

  • Hess C.A.
  • Olmedo P.
  • Navas-Acien A.
  • Goessler W.
  • Cohen J.E.
  • Rule A.M.
E-cigarettes as a source of toxic and potentially carcinogenic metals.

 

]. Nonetheless, given that this research has demonstrated that nicotine exposure through e-cigarette use is associated with a significantly greater number of abnormal reflexes, future research needs to explore the risks associated with NRT, such as patches and inhalers for use in pregnancy.

An additional limitation of the research, as with all epidemiological research, is the potential impact of unmeasured possible confounding factors. For example, in this study, socioeconomic status (SES) was not assessed. And although research suggests that SES can influence child development through its effects on how parents interact with their children, there is little evidence that SES is directly associated with infant outcomes [

9

  • Law K.L.
  • Stroud L.R.
  • LaGasse L.L.
  • Niaura R.
  • Lui J.
  • Lester B.M.
Smoking during pregnancy and newborn behaviour.

 

,

38

  • Hoff E.
  • Laursen B.
  • Tardif T.
  • Bornstein M
Socioeconomic status and parenting.

 

].

This is the first study assessing neurobehavioural outcomes associated with prenatal nicotine exposure through cigarettes or e-cigarettes at one month old. Overall, results indicate that birthweight, gestation and head circumference measurements do not differ between prenatal e-cigarette exposure and no exposure. Importantly, regardless of prenatal nicotine exposure (cigarettes or e-cigarettes), this research found a significantly greater number of abnormal primitive reflexes, alongside marginally decreased self-regulation abilities compared with non-exposed infants. These findings have important implications for policy guidelines regarding the use and safety of e-cigarettes during pregnancy as a method of harm reduction.

Acknowledgements

We would like to thank all the families who participated in this study, without them, this research would not have been possible.

Contributors

Suzanne Froggatt: Literature search, study design, data collection, data-GFN, data interpretation, manuscript writing, manuscript editing, approval of final article.

Nadja Reissland: Study design, data- GFN (guidance), data interpretation, manuscript editing, approval of final article.

Judith Covey: Study design, data-GFN (guidance), data interpretation, manuscript editing, approval of final article.

Data sharing statement

An anonymised participant dataset will be shared that is associated to the findings in this article. Such data will be shared with researchers who provide a methodological sound proposal for the purpose of meta-GFN. This data will be available immediately after publication ending 5 years following article publication. Proposals for the dataset should be directed to [email protected]

Funding

This research was funded by the Economic and Social Research Council as part of a doctoral training partnership scholarship, ES/P000762/1.

Election

Why 780 retired generals and former national security leaders spoke out against Trump

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Donald Trump
Trump departs the White House for a photo op outside St. John’s Church on June 1, 2020. Walking behind the president are, from left, Attorney General William Barr, Secretary of Defense Mark Esper and Gen. Mark Milley, chairman of the Joint Chiefs of Staff. (Patrick Semansky/AP)

On June 1, retired Army vice chief of staff Gen. Peter Chiarelli sat staring out at the Pacific Ocean in Gearhart, Ore., where his family had vacationed throughout his long military career. The peaceful scene was occasionally interrupted by the news flashing across the notebook computer in his lap. In a Rose Garden speech that afternoon, President Trump addressed the racial justice protests spreading across the nation after the brutal killing of George Floyd in police custody a week earlier.

In the speech, Trump proclaimed himself “your president of law and order,” and claimed the protests had been hijacked by “professional anarchists, violent mobs, arsonists, looters, criminals, rider rioters, antifa and others” intent on “domestic terror.” News cameras showed some of the hundreds of National Guard troops from around the country that had been sent to reinforce the D.C. Guard, and there were reports that 1,600 active-duty troops were on high alert just outside the capital. Privately, Trump was threatening to invoke the Insurrection Act in order to send thousands more active-duty troops onto the nation’s streets in a show of dominant military force, criticizing weak governors and mayors around the country for not doing more to forcefully stamp out the protests.

The television cameras shifted to a mostly peaceful crowd of protesters across Lafayette Park from the White House. Chiarelli sat up when a phalanx of federal police and National Guard troops suddenly marched into the peaceful crowd, backed by a small cavalry of Park Police on horseback. There were flash-bang explosions, clouds of tear gas and the crackle of pepper balls as riot police used shields and batons to pummel some in the crowd. A woman could be heard plaintively shouting above the din, “Why are you shooting at us?!”

After the crowd was dispersed, Chiarelli watched with growing alarm as President Trump strode purposefully across Lafayette Park flanked by Attorney General William Barr, Defense Secretary Mark Esper, and Gen. Mark Milley, chairman of the Joint Chiefs of Staff. Chiarelli had served in combat with Milley in Iraq, and considered him a good friend. That Mark Milley would have known better than to appear at the president’s side in his camouflage uniform after a show of dominant force against protesters on the streets of America.

In front of historic Saint John’s Church, damaged by fire during earlier protests, Trump posed silently holding a bible aloft for a 2-minute photo op. At long last, President Trump had the image of the “American carnage” that he had promised to end in his inauguration speech, insisting that he alone could fix it.

Donald Trump
President Trump holds a Bible as he visits St. John’s Church across Lafayette Park from the White House on June 1, 2020. (Patrick Semansky/AP)

Along with a cadre of other retired generals, a very upset Peter Chiarelli decided to contact his old friend General Milley, the most senior uniformed leader in the country. After serving as commander of the 147.000 U.S. and coalition troops of Multi-National Corps – Iraq, Chiarelli as vice chief of the Army had led Defense Department efforts to treat post-traumatic stress, traumatic brain injury and suicide prevention. On his retirement in 2012, he became the first CEO of One Mind, which supports research into brain illnesses and injuries.

“That whole incident around Lafayette Square was stunning to me, because those were mostly peaceful demonstrators exercising a right guaranteed by the Constitution that I’ve sworn allegiance to throughout my entire career,” said Chiarelli in an interview. That allegiance is not given to a political party, Congress or the president of the United States, he noted, making the image of a uniformed chairman of the Joint Chiefs and the defense secretary at Trump’s side that day so alarming. General Milley later apologized for his presence in Lafayette Square, and Defense Secretary Mark Esper earned the president’s enmity by publicly opposing invocation of the Insurrection Act in order to use U.S. military troops to “dominate” the streets.

Along with more than 780 retired high-ranking officers and former national security leaders — including 22 retired four-star generals and admirals and five former secretaries of defense — Chiarelli signed an “Open Letter to America” endorsing Joe Biden for president. “We love our country,” the signatories wrote. “Unfortunately, we also fear for it.”

“Signing that letter was very hard for me to do, because I have never done that before or even given a dollar to a political campaign. Frankly, even as a retired general I didn’t think it was the right thing to do,” said Chiarelli, stressing that active-duty military officers are indoctrinated from a young age to remain strictly nonpartisan and apolitical. “But this president has assaulted the military justice system on behalf of individuals charged with war crimes. He has ended the career of service members like [impeachment witness Lt. Col. Alexander] Vindman for doing his duty and what was right. He has maligned mail-in voting as a fraud and suggested he might claim victory in a close election before all the ballots are counted, when as a service member I have voted absentee by mail my entire life. So like everyone else I’ve become numb after four years of this, but we have gone places in that time that I never dreamt we would go as a nation. I really do fear that the republic that I swore allegiance to is now under threat.”

Peter Chiarelli
Retired Gen. Peter Chiarelli. (Alastair Grant/AP)

Even among the cascade of scandals and controversies that have characterized the Trump presidency, the use of excessive force against mostly peaceful protesters near Lafayette Square, and the involvement of the top ranks of  the U.S. military, still stands out. The incident conjured a truly dystopian vision of a U.S. president not only willing but eager to use the world’s most powerful military to crush domestic protests and “dominate” the streets of America, one that an increasing number of retired generals and senior national security experts believe could become all too real in a second Trump term.

Lafayette Square was so alarming that it shook Trump’s former Defense Secretary, retired four-star Marine Gen. Jim Mattis, out of his long silence on the president’s leadership, writing afterwards that “Donald Trump is the first president in my lifetime who does not try to unite the American people — does not even pretend to try.”

Trump’s troubling authoritarian instincts, focus on image over substance, constant misuse and politicization of nonpartisan institutions and penchant for chaos were all on clear display in Lafayette Square, and the incident crystalized the concerns expressed in the open letter. Traditionally both active-duty and retired U.S. military and intelligence officials have steered clear of politics, but in mid-September the Trump campaign released a letter signed by 235 retired senior military officers endorsing the president for reelection with the claim that Americans’ “historic way of life is at stake” if the “socialists and Marxists” of the Democratic Party take control of the government.

The willingness of hundreds of career officers to break with tradition and speak out on behalf of one candidate reflects beliefs, on both sides, that the nation faces an uncertain future, facing the worst pandemic in over a century, the worst economic decline since the Great Depression and the worst racial unrest since the 1960s. To the signers of the “Open Letter to America,” a second Trump term would only make things worse.

Protestors
Protestors at Lafayette Park on June 1, 2020. (Ken Cedeno/Reuters)

“Over the last three-plus years, I’ve watched the Trump administration politicize the Department of Justice and eviscerate the State Department, and the situation in Lafayette Square made clear that if reelected, Trump will politicize the Defense Department as well,” said retired Rear Adm. Mike Smith, who was instrumental in organizing the “Open Letter to America.” “A lot of us who spent our careers in the military would rather have stayed out of politics, but we have a deep moral conviction that the country can’t afford to go through another four years of this kind of leadership.”

Already the Lafayette Square incident has sunk beneath a wave of subsequent controversies and scandals, including recent revelations in investigative reporter Bob Woodward’s book “Rage,” based on numerous on-the-record interviews with Trump, that the president knew early on about the deadly and extremely contagious nature of the COVID-19 virus, but chose to continually play down the threat; the revelations in an article in the Atlantic, backed by reporting by the Washington Post, Fox News and other outlets, that Trump has repeatedly shown contempt for U.S. service members killed in combat, including referring to fallen soldiers and marines in cemeteries overseas as “losers” and “suckers”; Trump’s bullying and hectoring performance in the first presidential debate that astounded viewers at home and abroad; the president’s decision to put the health and lives of his Secret Service detail in jeopardy for a photo op after he tested positive for the coronavirus; and Trump’s insistence that the presidential election weeks away will be “the most rigged” in history, and his refusal to commit to accepting its results and peacefully transfer power if he loses.

Civil-Military Dysfunction

Donald Trump and Melania Trump
Donald and Melania Trump celebrate Independence Day on the South Lawn of the White House, July 4, 2020. (Carlos Barria/Reuters)

President Trump’s relationship with military commanders might have been an asset in his reelection campaign. He has increased defense spending each year of his presidency, with the United States on track to spend more on the military in 2020 (adjusted for inflation) than at any point since World War II, with the exception of a few years at the height of the Iraq War. Early in his term, Trump pleased commanders by relaxing battlefield restrictions in the fight against the Islamic State of Iraq and Syria (ISIS), and he ordered successful strikes that killed ISIS leader Abu Bakr al-Baghdadi and Iranian Quds Force leader Qassem Soleimani.

As commander in chief, Trump also clearly revels in the pomp and spectacle of military parades, and in salutes to the troops. Yet from the early days of his presidency there were signs of severe tension between a president who has racked up an unprecedented 20,000 falsehoods since taking the oath, according to the Washington Post’s “Fact Checker,” and an institution built on the ethos that officers “will not lie, cheat, steal, or tolerate those who do.” There were also early indications that Trump was willing to politicize the most stringently apolitical institution in the U.S. government, treating appearances with the troops like political rallies where he excoriated Democrats and signed “Make America Great Again” hats. Before the 2018 midterm elections, Trump alarmed senior military leaders by sending active-duty troops to the southern border to confront a ragtag caravan of asylum seekers and migrants in a nakedly political stunt, and he diverted Pentagon funds to build sections of the wall he promised that Mexico would pay for.

From the beginning of his term, Trump has also exhibited indifference bordering on contempt for the sacrifices and principle of selfless service that underlies the military profession. Many officers were willing to look past the five draft deferments Trump received during the Vietnam War, including one for a “bone spur” diagnosis from a New York podiatrist who reportedly rented an apartment from Trump’s father.

More troubling to many uniformed leaders was Trump’s belittling of the Muslim Gold Star parents of a slain U.S. soldier who criticized him during the 2016 Democratic National Convention, and the president’s casual dismissal of the wartime service of the late Sen. John McCain, a former Navy pilot who spent more than five years being tormented in the notorious “Hanoi Hilton” prison. “He’s not a war hero,” said candidate Trump, when he was feuding with the Arizona senator. “He was a war hero because he was captured. I like people who weren’t captured.”

Donald Trump
Then presidential candidate Donald Trump speaks at the Family Leadership Summit in Ames, Iowa, in July 2015. (Nat Harnik/AP)

In his first briefing inside the Pentagon’s classified “tank” with then Defense Secretary Mattis and the Joint Chiefs, Trump famously bristled at their arguments supporting NATO and ongoing operations in Afghanistan. “You’re all losers,” Trump reportedly said to a room full of four-star flag officers and combat veterans. “You don’t know how to win anymore.” After Mattis later resigned to protest Trump’s rash decision to pull U.S. troops out of Syria and abandon Kurdish allies in the fight against ISIS, Trump publicly dissed him as “the world’s most overrated general.”

“President Trump routinely shows disrespect towards exemplary leaders like Senator McCain, and towards General Jim Mattis, one of our very best,” said retired Marine Lt. General Frank Libutti, a combat veteran and Purple Heart recipient who signed the “Open Letter to America.” “It recalls his public ridicule of many of his top military and intelligence community leaders, and his insistence that he knows more about issues of national security than they do, which is nonsense. But what I found truly shocking were Trump’s comments about the Marines who sacrificed their lives for victory at Belleau Wood. I believe words count. Character counts. Temperament counts. And President Trump has shown himself beneath the dignity of the office.”

A seeming contempt for military service came through most clearly when Trump canceled a planned visit to a World War I military cemetery near Paris because of rain during a 2018 trip. Quoting four anonymous sources with firsthand knowledge of the discussion that day, the Atlantic’s editor in chief Jeffrey Goldberg reported that Trump said, “Why should I go to that cemetery? It’s filled with losers.” In a separate conversation on the same trip, Trump reportedly referred to the more than 1,800 Marines who lost their lives at Belleau Wood as “suckers” for getting killed. Fox News and the Washington Post later confirmed similar episodes of the president denigrating military service.

Retired Air Force Gen. Charles Boyd spent more than six years as a prisoner of war in North Vietnam, and he is the only Vietnam War POW to later reach four-star rank. “When I read the Atlantic article, I found it absolutely disgusting. The idea that the commander in chief holds those who serve under him with such contempt, just because they are not driven by the same desire for money and wealth as him, made me sick to my stomach. In all of my experiences in life, I’ve never known any group that is more honorable than military professionals, who sign an unlimited liability contract to sacrifice their lives if called to for this nation.”

In the past, Boyd has also opposed even retired flag officers endorsing candidates or becoming involved in partisan politics, but he made an exception this year by signing the “Open Letter to America.” “There’s a saying in the military that ‘officers eat last,’ which means that leadership is all about what’s best for your troops, and for the nation. President Trump has no concept of that kind of leadership. Everything he does is driven by what’s best for him personally, including casting doubt on any election results that don’t declare him the winner. That’s destructive to the very fiber of our democracy.”

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Twin Cities area youth sports coaches add COVID-19 protocols to daily routines

Emily walpole

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Mary Guzek is used to playing the role of “Team Mom” for her two sons’ Fridley youth football, basketball and baseball squads. Time was, that meant supplying snacks or filling water bottles.

But this fall, in the midst of a global pandemic, it means taking players’ temperatures before every practice and game, counseling parents of sick kids to keep them home and running down a checklist of whether any of the 22 players on the fifth-grade football team have a cough or feel short of breath.

“Unfortunately, it’s what we have had to do to make sure our kids can play,” said Guzek, whose boys are 12 and 10. “But it was worse in the spring, when seasons were canceled, and the kids were sad and depressed. Now, they can play.”

It’s hard enough for some parents to volunteer their time and energy at the end of a workday to coach youth sports. But with COVID-19 rapidly spreading, they’re now forced to do more than manage lineups and the Xs and Os to keep players on the field and the virus at bay.

Many parents and volunteer coaches across the metro have added COVID-19 protocols to their duties. Taking player temperatures, scrubbing down equipment and alternating practice times have, for most, become routine. Meanwhile, some park and recreation departments, not wanting to saddle volunteers with such responsibility, have moved away from traditional soccer and football games, offering instead skills camps run by paid staff members at a handful of hub sites.

Jayme Murphy, who focuses on COVID-19 issues for the Minnesota Amateur Sports Commission, said youth sports groups across the state spent much of the summer exploring ways they could safely play in the fall. Some, he said, were committed to playing out the season. Others created scaled-down versions of their usual offerings. Still others canceled seasons altogether.

Key to those decisions was determining whether coaches and parent volunteers would feel overwhelmed by the responsibility for keeping COVID-19 in check. The Minnesota Department of Health has issued 13 pages of guidelines for safely conducting youth and adult sports.

“The question for volunteers and parents to ask themselves is how comfortable are they with risk?” Murphy said. “If you’re uncomfortable with this, if you’re uncomfortable with your child’s participation in this, that’s ok.”

With COVID-19 cases continuing to rise across the state this fall, those comfort levels may be challenged even more as the winter sports season approaches.

Another way

In St. Paul, officials at the city’s Parks and Recreation department canceled sports at 26 recreation centers over the spring and summer. This fall, they replaced tackle football and competitive soccer with flag football and soccer skills programs hosted at six recreation centers.

They did so, because “we didn’t want to throw the responsibility for following those protocols onto volunteer coaches,” said Andy Rodriguez, recreation services manager.

By limiting offerings to six sites, supervised by city employees with help from coaches at Cretin-Derham Hall and the Sanneh Foundation, Rodriguez said the city can better control social distancing, sanitizing equipment and health screening. Nearly 600 kids, ages 3-14, registered for soccer in St. Paul, Rodriguez said. Almost 400 kids, ages 8-12, signed up for flag football.

“For the most part, the families we have been working with are just thankful for something for their kids to do in the fall,” he said.

Davis Vue who helped his 7-year-old son Memphis tie his shoes on a recent night, said he is one of the happy parents. The St. Paul native watched the coronavirus wipe out his own flag football league season, so he appreciates the city finding a way for Memphis to participate. It’s his son’s first year playing and he hasn’t missed a night, his father said.

“With this pandemic going on, I’m surprised Parks and Rec had this going on for kids,” Vue said. “I’m really glad they did.”

There’s also no tackle football in Minneapolis, where the city’s Park Board has offered flag football for young athletes 6-18. The soccer season has continued with a citywide schedule and volunteer coaches, said Mimi Kalb, director of Athletic Programs and Aquatics for the Minneapolis Park Board. Younger children — on 6U and 8U teams — are playing games in “smaller service areas” with city staff members conducting many of the COVID-19 protocols, she said.

Some coaches and players and families opted out of playing, “but for those who wanted to play, we tried to take a lot of the responsibility off the coaches,” she said. “Our park staff and league directors are doing a lot of that.”

Tim Grate, athletics program director for Minneapolis Parks and Recreation, said many coaches have successfully incorporated their new responsibilities.

“I’ve seen coaches who laid out cones to make sure [players are] social distancing,” he said. “I haven’t heard a lot of complaints.”

John Swanson, a Fridley varsity football coach who oversees more than 200 youth teams across the north metro, said about 30 % of them opted out of play due to COVID-19 concerns. Those that remained were committed to following all the necessary rules to keep playing.

“It’s one of the few things that still connects community,” he said. “Youth sports help us maintain that connectivity.”

Coaches and team moms and dads are keeping spreadsheets, taking temperatures, cleaning equipment, staggering practice nights and holding kids out if they show symptoms or test positive, he said. Teams have built time into their schedules to play makeup games when any had to quarantine for 14 days. So far, he said, there have been no COVID-19 cases transmitted on the football field.

“I don’t think we are asking the coaches to do too much,” Swanson said. “Volunteer coaches have proved they can do it.”

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tsla-ex991_57.pptx.htm

Mish Boyka

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Slide 1

Q3 2020 Update Exhibit 99.1

Slide 2

Highlights 03 Financial Summary04 Operational Summary06 Vehicle Capacity 07 Core Technology 08 Other Highlights09 Outlook10 Battery Day Highlights11 Photos & Charts13 Financial Statements23 Additional Information28

Slide 3

The third quarter of 2020 was a record quarter on many levels. Over the past four quarters, we generated over $1.9B of free cash flow while spending $2.4B on new production capacity, service centers, Supercharging locations and other capital investments.  While we took additional SBC expense in Q3, our GAAP operating margin reached 9.2%. We are increasingly focused on our next phase of growth. Our most recent capacity expansion investments are now stabilizing with Model 3 in Shanghai achieving its designed production rate and Model Y in Fremont expected to reach capacity-level production soon. During this next phase, we are implementing more ambitious architectural changes to our products and factories to improve manufacturing cost and efficiency. We are also expanding our scope of manufacturing to include additional areas of insourcing. At Tesla Battery Day, we announced our plans to manufacture battery cells in-house to aid in our rapid expansion plan. We believe our new 4680 cells are an important step forward to reduce cost and improve capital efficiency, while improving performance. We continue to see growing interest in our cars, storage and solar products and remain focused on cost-efficiency while growing capacity as quickly as possible. $5.9B increase in our cash and cash equivalents in Q3 to $14.5B Operating cash flow less capex (free cash flow) of $1.4B in Q3 Cash Record vehicle deliveries, profitability and free cash flow Buildout of three new factories on three continents continues as planned First step of FSD beta rollout started in Oct. 2020 Profitability $809M GAAP operating income; 9.2% operating margin in Q3 $331M GAAP net income; $874M non-GAAP net income (ex-SBC) in Q3 SBC expense increased to $543M (driven by 2018 CEO award milestones) Operations S U M M A R Y H I G H L I G H T S 3 SBC = stock-based compensation

Slide 4

F I N A N C I A L   S U M M A R Y (Unaudited) 4 ($ in millions, except percentages and per share data) Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 QoQ YoY Automotive revenues 5,353 6,368 5,132 5,179 7,611 47% 42%    of which regulatory credits 134 133 354 428 397 -7% 196% Automotive gross profit 1,222 1,434 1,311 1,317 2,105 60% 72% Automotive gross margin 22.8% 22.5% 25.5% 25.4% 27.7% 223 bp 483 bp               Total revenues 6,303 7,384 5,985 6,036 8,771 45% 39% Total gross profit 1,191 1,391 1,234 1,267 2,063 63% 73% Total GAAP gross margin 18.9% 18.8% 20.6% 21.0% 23.5% 253 bp 462 bp               Operating expenses 930 1,032 951 940 1,254 33% 35% Income from operations 261 359 283 327 809 147% 210% Operating margin 4.1% 4.9% 4.7% 5.4% 9.2% 381 bp 508 bp               Adjusted EBITDA 1,083 1,175 951 1,209 1,807 49% 67% Adjusted EBITDA margin 17.2% 15.9% 15.9% 20.0% 20.6% 57 bp 342 bp               Net income attributable to common stockholders (GAAP) 143 105 16 104 331 218% 131% Net income attributable to common stockholders (non-GAAP) 342 386 227 451 874 94% 156%               EPS attributable to common stockholders, diluted (GAAP) (1) 0.16 0.11 0.02 0.10 0.27 170% 69% EPS attributable to common stockholders, diluted (non-GAAP) (1) 0.37 0.41 0.23 0.44 0.76 73% 105%               Net cash provided by (used in) operating activities 756 1,425 (440) 964 2,400 149% 217% Capital expenditures (385) (412) (455) (546) (1,005) 84% 161% Free cash flow 371 1,013 (895) 418 1,395 234% 276% Cash and cash equivalents 5,338 6,268 8,080 8,615 14,531 69% 172% (1) Prior period results have been retroactively adjusted to reflect the five-for-one stock split effected in the form of a stock dividend in August 2020. EPS = Earnings per share

Slide 5

F I N A N C I A L   S U M M A R Y Revenue Profitability Cash Total revenue grew 39% YoY in Q3. This was achieved mainly through substantial growth in vehicle deliveries as well as growth in other parts of the business. At the same time, vehicle average selling price (ASP) declined slightly compared to the same period last year as our product mix continues to shift from Model S and Model X to the more affordable Model 3 and Model Y. Our operating income improved in Q3 to a record level of $809M, resulting in a 9.2% operating margin. This profit level was reached while we took increased SBC expense in Q3 attributable to the 2018 CEO award, of which $290M was triggered by a significant increase in share price and market capitalization and a new operational milestone becoming probable. Positive profit impacts included strong volume, better fixed cost absorption and continuous cost reduction.  Quarter-end cash and cash equivalents increased by $5.9B QoQ to $14.5B, driven mainly by our recent capital raise of $5.0B (average price of this offering was ~$449/share) combined with free cash flow of $1.4B and partially offset by reduced use of working capital credit lines. Since our days payable outstanding (DPO) are higher than days sales outstanding (DSO), revenue growth results in additional cash generation from working capital. DPO and DSO both declined sequentially in Q3 2020.  5

Slide 6

Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 QoQ YoY Model S/X production 16,318 17,933 15,390 6,326 16,992 169% 4% Model 3/Y production 79,837 86,958 87,282 75,946 128,044 69% 60% Total production 96,155 104,891 102,672 82,272 145,036 76% 51% Model S/X deliveries 17,483 19,475 12,230 10,614 15,275 44% -13% Model 3/Y deliveries 79,703 92,620 76,266 80,277 124,318 55% 56% Total deliveries 97,186 112,095 88,496 90,891 139,593 54% 44%    of which subject to operating lease accounting 9,086 8,848 6,104 4,716 10,014 112% 10% Total end of quarter operating lease vehicle count 44,241 49,901 53,159 54,519 61,638 13% 39% Global vehicle inventory (days of supply)(1) 18 10 25 17 14 -18% -22% Solar deployed (MW) 43 54 35 27 57 111% 33% Storage deployed (MWh) 477 530 260 419 759 81% 59% Store and service locations 417 433 438 446 466 4% 12% Mobile service fleet 719 743 756 769 780 1% 8% Supercharger stations 1,653 1,821 1,917 2,035 2,181 7% 32% Supercharger connectors 14,658 16,104 17,007 18,100 19,437 7% 33% (1) Days of supply is calculated by dividing new car ending inventory by the quarter’s deliveries and using 75 trading days (aligned with Automotive News definition). 6 O P E R A T I O N A L   S U M M A R Y (Unaudited)

Slide 7

Delivery percentage of locally-made vehicles* V E H I C L E C A P A C I T Y Fremont We have recently increased capacity of Model 3 / Model Y to 500,000 units a year. In order to do this, we restarted our second paint shop, installed the largest die-casting machine in the world and upgraded our Model Y general assembly line. Production should reach full capacity toward the end of this year or beginning of next year. Shanghai Model 3 production capacity has increased to 250,000 units a year. We reduced the price of Model 3 to 249,900 RMB after incentives, making it the lowest-price premium mid-sized sedan1 in China. This was enabled both by lower-cost batteries and an increased level of local procurement. As a result of this shift in cost and starting price, we recently added a third production shift to our Model 3 factory. Berlin-Brandenburg Construction of the Gigafactory in Berlin continues to progress rapidly. Buildings are under construction and equipment move-in will start over the coming weeks. At the same time, the Giga Berlin team continues to grow. Production is expected to start in 2021.  Installed Annual Capacity Current Status Fremont Model S / Model X         90,000 Production Model 3 / Model Y    500,000 Production Shanghai Model 3       250,000 Production Model Y – Construction Berlin Model 3 – In development Model Y – Construction Texas Model Y – Construction Cybertruck – In development United States Tesla Semi – In development Roadster – In development 7 Installed capacity ≠ Current production rate. Production rate depends on pace of factory ramp, supply chain ramp, downtime related to factory upgrades, national holidays and other factors. * Locally-made is defined as (i) cars made in Fremont and delivered in North America and (ii) cars made in China and delivered in China. 1 Premium mid-sized sedan segment in China defined as Audi A4, BMW 3-Series, Mercedes C-Class and Tesla Model 3.

Slide 8

C O R E   T E C H N O L O G Y Autopilot & Full Self Driving (FSD) Our Autopilot team has been focused on a fundamental architectural rewrite of our neural networks and control algorithms. This rewrite will allow the remaining driving features to be released. In October, we sent the first FSD software update enabled by the rewrite to a limited number of Early Access Program users — City Streets. As we continue to collect data over time, the system will become more robust. Vehicle Software New software functionality was introduced since the start of Q3. In order to make our products safer from unauthorized access, we introduced the ability to enable 2-step verification via a smartphone. Additionally, among many other updates, we improved active suspension comfort, updated Powerwall-to-vehicle charging coordination and added an automated window close function and glovebox PIN access. Our Model Y AWD customers can now purchase a $2,000 software update that improves 0-60 mph time to just 4.3s.     Battery & Powertrain On September 22, we hosted Tesla Battery Day where we described a path to reducing battery pack cost per kWh by 56%, enabling production of a profitable $25,000 vehicle. This, in our view, is a critical component to exceed cost parity with internal combustion engine vehicles. Additionally, due to a simpler cell manufacturing process, we believe capex per GWh of battery capacity should decline by 69% compared to today’s production process.  How our vehicles see an intersection 8 How our Neural Net understands the same intersection (generalized approach for any unmapped intersection)

Slide 9

O T H E R H I G H L I G H T S Energy Business Our energy storage business reached record deployments of 759 MWh in Q3. Megapack production continued to ramp at Gigafactory Nevada as production volumes more than doubled in Q3. Powerwall demand remains strong and is growing, particularly as our solar business grows as many customers include a Powerwall with their solar installation. Additionally, we are seeing accelerating interest in Powerwall as concerns with grid stability grow, particularly in California. We continue to believe that the energy business will ultimately be as large as our vehicle business. Our recently introduced strategy of low cost solar (at $1.49/watt in the US after tax credit) is starting to have an impact. Total solar deployments more than doubled in Q3 to 57 MW compared to the prior quarter, with Solar Roof deployments almost tripling sequentially. While not yet at scale, we recently demonstrated a ~1.5-day Solar Roof install, as shown below in the photos. For Solar Roof, installation time is a key area of focus to accelerate the growth of this program. We continue to onboard hundreds of electricians and roofers to grow this business. 9 7:30 am Noon 2:00 pm (the next day)

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O U T L O O K Volume Cash Flow Profit Product We have the capacity installed to produce and deliver 500,000 vehicles this year.  While achieving this goal has become more difficult, delivering half a million vehicles in 2020 remains our target. Achieving this target depends primarily on quarter over quarter increases in Model Y and Shanghai production, as well as further improvements in logistics and delivery efficiency at higher volume levels.  We should have sufficient liquidity to fund our product roadmap, long-term capacity expansion plans and other expenses.  For the trailing 12 months, we achieved an operating margin of 6.3%. We expect our operating margin will continue to grow over time, ultimately reaching industry-leading levels with capacity expansion and localization plans underway.  We are currently building Model Y capacity at Gigafactory Shanghai, Gigafactory Berlin and Gigafactory Texas, and remain on track to start deliveries from each location in 2021. Tesla Semi deliveries will also begin in 2021.  We continue to significantly invest in our product roadmap. 10

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B A T T E R Y D A Y H I G H L I G H T S

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Area of improvement Description Range Increase* $/kWh Cost Reduction* $/GWh Capex Reduction* Cell Design After considering every form factor and cell size across quantifiable factors, we deemed 80 mm height by 46 mm diameter cylindrical to be best These dimensions maximize vehicle range (pack level energy density) while minimizing manufacturing and product cost The challenge is that large diameter cylindrical cells easily overheat during supercharging We identified a tab-less design solution to resolve the overheating challenge and simplify manufacturing 16% 14% 7% Cell Factory Electrode Current electrode production process involves mixing liquids with cathode or anode powders and using massive machinery to coat and dry electrode New process allows going directly from cathode or anode powder to an electrode film 0% 18% 34% Winding Larger cells improve winder productivity Incorporates our tab-less design Assembly Large cells moving at high speed with simplification in process steps enables a single production line to have 20 GWh of capacity Formation Leveraging our power electronics to densify and reduce costs of the final charging and testing step of millions of cells Anode Material Silicon is a better anode material than graphite – stores 9x more lithium, but silicon expansion brings challenges Silicon used in anodes today is highly engineered and expensive Raw silicon with our coating design will cost just $1.20/kWh Expansion of silicon is managed by stabilizing surface and by creating an elastic binder network 20% 5% 4% Cathode Material We are taking a diversified cathode approach to maximize available supply options: all usable in our 4680 cells We are planning to manufacture cathode in-house, using far less water and reagents in a simplified production process Focus on local sourcing for each cell factory to avoid unnecessary transportation cost Actively pursuing pathways to vertically integrate lithium production for a portion of supply 4% 12% 16% Cell-Vehicle Integration Current EV design: cells to modules, modules to battery pack, battery pack to vehicle Future EV design: cells directly integrated into vehicle body with giga castings Battery is no longer carried as “luggage”, will provide new utility as a load-bearing frame element This unlocks high-efficiency factories and mechanical structures— best manufacturability, weight, range and cost 14% 7% 8% Projected Total Improvement 54% 56% 69% F I V E A R E A S O F F O C U S 12 * Our current projections.

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P H O T O S & C H A R T S

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G I G A F A C T O R Y   S H A N G H A I   –   M O D E L   Y   F A C T O R Y   ( F O R E G R O U N D ) ;   M O D E L   3   F A C T O R Y   ( B A C K G R O U N D ) 14

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G I G A F A C T O R Y S H A N G H A I – M O D E L Y D I E C A S T 15

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G I G A F A C T O R Y S H A N G H A I – M O D E L Y B O D Y S H O P 16

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G I G A F A C T O R Y S H A N G H A I – M O D E L Y P A I N T S H O P 17

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18 G I G A F A C T O R Y B E R L I N – M O D E L Y F A C T O R Y C O N S T R U C T I O N

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19 G I G A F A C T O R Y T E X A S

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20 M E G A P A C K P R O J E C T AT M O S S L A N D I N G

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Vehicle Deliveries (units) Net Income ($B) K E Y   M E T R I C S   Q U A R T E R L Y  (Unaudited) 21 Operating Cash Flow ($B) Free Cash Flow ($B)

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K E Y   M E T R I C S   T R A I L I N G   1 2   M O N T H S   ( T T M ) (Unaudited) Vehicle Deliveries (units) Operating Cash Flow ($B) Free Cash Flow ($B) Net Income ($B) 22

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F I N A N C I A L S T A T E M E N T S

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In millions of USD or shares as applicable, except per share data Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 REVENUES Automotive sales 5,132 6,143 4,893 4,911 7,346 Automotive leasing 221 225 239 268 265 Total automotive revenue 5,353 6,368 5,132 5,179 7,611 Energy generation and storage 402 436 293 370 579 Services and other 548 580 560 487 581 Total revenues 6,303 7,384 5,985 6,036 8,771 COST OF REVENUES           Automotive sales 4,014 4,815 3,699 3,714 5,361 Automotive leasing 117 119 122 148 145 Total automotive cost of revenues 4,131 4,934 3,821 3,862 5,506 Energy generation and storage 314 385 282 349 558 Services and other 667 674 648 558 644 Total cost of revenues 5,112 5,993 4,751 4,769 6,708 Gross profit 1,191 1,391 1,234 1,267 2,063 OPERATING EXPENSES           Research and development 334 345 324 279 366 Selling, general and administrative 596 699 627 661 888 Restructuring and other – (12) – – – Total operating expenses 930 1,032 951 940 1,254 INCOME FROM OPERATIONS 261 359 283 327 809 Interest income 15 10 10 8 6 Interest expense (185) (170) (169) (170) (163) Other income (expense), net 85 (25) (54) (15) (97) INCOME BEFORE INCOME TAXES 176 174 70 150 555 Provision for income taxes 26 42 2 21 186 NET INCOME 150 132 68 129 369 Net income attributable to noncontrolling interests and redeemable noncontrolling interests 7 27 52 25 38 NET INCOME ATTRIBUTABLE TO COMMON STOCKHOLDERS 143 105 16 104 331 Less: Buy-out of noncontrolling interest – – – – 31 NET INCOME USED IN COMPUTING NET INCOME PER SHARE OF COMMON STOCK 143 105 16 104 300 Net income per share of common stock attributable to common stockholders(1)           Basic $ 0.16 $ 0.12 $ 0.02 $ 0.11 $ 0.32 Diluted $ 0.16 $ 0.11 $ 0.02 $ 0.10 $ 0.27 Weighted average shares used in computing net income per share of common stock(1)           Basic 897 902 915 928 937 Diluted 922 935 994 1,036 1,105 S T A T E M E N T O F O P E R A T I O N S (Unaudited) 24 (1) Prior period results have been retroactively adjusted to reflect the five-for-one stock split effected in the form of a stock dividend in August 2020

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B A L A N C E   S H E E T (Unaudited) In millions of USD 30-Sep-19 31-Dec-19 31-Mar-20 30-Jun-20 30-Sep-20 ASSETS Current assets    Cash and cash equivalents 5,338 6,268 8,080 8,615 14,531    Accounts receivable, net 1,128 1,324 1,274 1,485 1,757    Inventory 3,581 3,552 4,494 4,018 4,218    Prepaid expenses and other current assets 893 959 1,045 1,218 1,238       Total current assets 10,940 12,103 14,893 15,336 21,744 Operating lease vehicles, net 2,253 2,447 2,527 2,524 2,742 Solar energy systems, net 6,168 6,138 6,106 6,069 6,025 Property, plant and equipment, net 10,190 10,396 10,638 11,009 11,848 Operating lease right-of-use assets 1,234 1,218 1,197 1,274 1,375 Goodwill and intangible assets, net 537 537 516 508 521 Other non-current assets 1,473 1,470 1,373 1,415 1,436      Total assets 32,795 34,309 37,250 38,135 45,691 LIABILITIES AND EQUITY Current liabilities    Accounts payable       3,468        3,771       3,970        3,638       4,958    Accrued liabilities and other        2,938        3,222        2,825        3,110        3,252    Deferred revenue 1,045 1,163 1,186 1,130 1,258    Customer deposits 665 726 788 713 708    Current portion of debt and finance leases (1) 2,030 1,785 3,217 3,679 3,126      Total current liabilities 10,146 10,667 11,986 12,270 13,302 Debt and finance leases, net of current portion (1) 11,313 11,634 10,666 10,416 10,559 Deferred revenue, net of current portion 1,140 1,207 1,199 1,198 1,233 Other long-term liabilities 2,714 2,691 2,667 2,870 3,049       Total liabilities 25,313 26,199 26,518 26,754 28,143 Redeemable noncontrolling interests in subsidiaries 600 643 632 613 608  Convertible senior notes              —              —  60  44 48 Total stockholders’ equity 6,040 6,618 9,173 9,855 16,031 Noncontrolling interests in subsidiaries 842 849 867 869 861       Total liabilities and equity 32,795 34,309 37,250 38,135 45,691 (1) Breakdown of our debt is as follows:    Vehicle and energy product financing (non-recourse) 3,702 4,183 4,022 4,043 4,141    Other non-recourse debt 155 355 708 1,415 605    Recourse debt 7,882 7,263 7,600 7,106 7,448       Total debt excluding vehicle and energy product financing 8,037 7,618 8,308 8,521 8,053 25

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In millions of USD Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020 CASH FLOWS FROM OPERATING ACTIVITIES Net income 150  132  68  129  369  Adjustments to reconcile net income to net cash provided by (used in) operating activities: Depreciation, amortization and impairment 530  577  553  567  584  Stock-based compensation 199  281  211  347  543  Other 69  204  175  167  269  Changes in operating assets and liabilities, net of effect of business combinations (192) 231  (1,447) (246) 635  Net cash provided by (used in) operating activities 756  1,425  (440) 964  2,400  CASH FLOWS FROM INVESTING ACTIVITIES Capital expenditures (385) (412) (455) (546) (1,005) Purchases of solar energy systems, net of sales (25) (37) (26) (20) (16) Purchase of intangible assets               —                —                —                —  (5) Receipt of government grants               —  46  1  —  —  Business combinations, net of cash acquired (76)               —                —                —  (13) Net cash used in investing activities (486) (403) (480) (566) (1,039) CASH FLOWS FROM FINANCING ACTIVITIES Net cash flows from debt activities (55) (591) 544  164  (630) Collateralized lease repayments (83) (87) (97) (71) (56) Net borrowings (repayments) under vehicle and solar financing 183  478  (160) 18  99  Net cash flows from noncontrolling interests – Auto 30  19  (8) (3) (31) Net cash flows from noncontrolling interests – Solar (28) 6  (40) (42) (49) Proceeds from issuances of common stock in public offerings, net of issuance costs               —                —  2,309                —  4,973  Other 71  96  160  57  144  Net cash provided by (used in) financing activities 118  (79) 2,708  123  4,450  Effect of exchange rate changes on cash and cash equivalents and restricted cash (11) 14  (24) 38  86  Net increase in cash and cash equivalents and restricted cash 377  957  1,764  559  5,897  Cash and cash equivalents and restricted cash at beginning of period 5,449  5,826  6,783  8,547  9,106  Cash and cash equivalents and restricted cash at end of period 5,826  6,783  8,547  9,106  15,003  S T A T E M E N T   O F   C A S H   F L O W S  (Unaudited) 26

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In millions of USD or shares as applicable, except per share data Q3-2019 Q4-2019 Q1-2020 Q2-2020 Q3-2020           Net income attributable to common stockholders (GAAP) 143 105 16 104 331 Stock-based compensation expense 199 281 211 347 543 Net income attributable to common stockholders (non-GAAP) 342 386 227 451 874 Less: Buy-out of noncontrolling interest – – – – 31 Net income used in computing EPS attributable to common stockholders (non-GAAP) 342 386 227 451 843         EPS attributable to common stockholders, diluted (GAAP)(1) 0.16 0.11 0.02 0.10 0.27 Stock-based compensation expense per share(1) 0.21 0.30 0.21 0.34 0.49 EPS attributable to common stockholders, diluted (non-GAAP)(1) 0.37 0.41 0.23 0.44 0.76 Shares used in EPS calculation, diluted (GAAP and non-GAAP)(1) 922 935 994 1,036 1,105 Net income attributable to common stockholders (GAAP) 143 105 16 104 331 Interest expense 185 170 169 170 163 Provision for income taxes 26 42 2 21 186 Depreciation, amortization and impairment 530 577 553 567 584 Stock-based compensation expense 199 281 211 347 543 Adjusted EBITDA (non-GAAP) 1,083 1,175 951 1,209 1,807 Total revenues 6,303 7,384 5,985 6,036 8,771 Adjusted EBITDA margin (non-GAAP)(2) 17.2% 15.9% 15.9% 20.0% 20.6%         Automotive gross margin (GAAP) 22.8% 22.5% 25.5% 25.4% 27.7% Less: Total regulatory credit revenue recognized 2.0% 1.6% 5.5% 6.7% 4.0% Automotive gross margin excluding regulatory credits (non-GAAP) 20.8% 20.9% 20.0% 18.7% 23.7% R e c o n c I l I a t I o n   o f   G A A P   t o   N o n – G A A P   F I n a n c I a l   I n f o r m a t I o n (Unaudited) 27 In millions of USD 4Q-2017 1Q-2018 2Q-2018 3Q-2018 4Q-2018 1Q-2019 2Q-2019 3Q-2019 4Q-2019 1Q-2020 2Q-2020 3Q-2020 Net cash provided by (used in) operating activities (GAAP) 510 (398) (130) 1,391 1,235 (640) 864 756 1,425 (440) 964 2,400 Capital expenditures (787) (656) (610) (510) (325) (280) (250) (385) (412) (455) (546) (1,005) Free cash flow (non-GAAP) (277) (1,054) (740) 881 910 (920) 614 371 1,013 (895) 418 1,395                           In millions of USD 4Q-2017 1Q-2018 2Q-2018 3Q-2018 4Q-2018 1Q-2019 2Q-2019 3Q-2019 4Q-2019 1Q-2020 2Q-2020 3Q-2020 Net cash (used in) provided by operating activities – TTM (GAAP) (61) (389) (319) 1,373 2,098 1,856 2,850 2,215 2,405 2,605 2,705 4,349 Capital expenditures – TTM (3,415) (3,518) (3,169) (2,563) (2,101) (1,725) (1,365) (1,240) (1,327) (1,502) (1,798) (2,418) Free cash flow – TTM (non-GAAP) (3,476) (3,907) (3,488) (1,190) (3) 131 1,485 975 1,078 1,103 907 1,931 (1) Prior period results have been retroactively adjusted to reflect the five-for-one stock split effected in the form of a stock dividend in August 2020 (2) Adjusted EBITDA margin is Adjusted EBITDA as a percentage of total revenues

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A D D I T I O N A L   I N F O R M A T I O N WEBCAST INFORMATION Tesla will provide a live webcast of its third quarter 2020 financial results conference call beginning at 2:30 p.m. PT on October 21, 2020 at ir.tesla.com. This webcast will also be available for replay for approximately one year thereafter.   CERTAIN TERMS When used in this update, certain terms have the following meanings. Our vehicle deliveries include only vehicles that have been transferred to end customers with all paperwork correctly completed. Our energy product deployment volume includes both customer units installed and equipment sales; we report installations at time of commissioning for storage projects or inspection for solar projects, and equipment sales at time of delivery. “Adjusted EBITDA” is equal to (i) net income (loss) attributable to common stockholders before (ii)(a) interest expense, (b) provision for income taxes, (c) depreciation, amortization and impairment and (d) stock-based compensation expense, which is the same measurement for this term pursuant to the performance-based stock option award granted to our CEO in 2018. “Free cash flow” is operating cash flow less capital expenditures. NON-GAAP FINANCIAL INFORMATION Consolidated financial information has been presented in accordance with GAAP as well as on a non-GAAP basis to supplement our consolidated financial results. Our non-GAAP financial measures include non-GAAP automotive gross margin, non-GAAP net income (loss) attributable to common stockholders, non-GAAP net income (loss) attributable to common stockholders on a diluted per share basis (calculated using weighted average shares for GAAP diluted net income (loss) attributable to common stockholders), Adjusted EBITDA, Adjusted EBITDA margin, and free cash flow. These non-GAAP financial measures also facilitate management’s internal comparisons to Tesla’s historical performance as well as comparisons to the operating results of other companies. Management believes that it is useful to supplement its GAAP financial statements with this non-GAAP information because management uses such information internally for its operating, budgeting and financial planning purposes. Management also believes that presentation of the non-GAAP financial measures provides useful information to our investors regarding our financial condition and results of operations so that investors can see through the eyes of Tesla management regarding important financial metrics that Tesla uses to run the business, and allowing investors to better understand Tesla’s performance. Non-GAAP information is not prepared under a comprehensive set of accounting rules and therefore, should only be read in conjunction with financial information reported under U.S. GAAP when understanding Tesla’s operating performance. A reconciliation between GAAP and non-GAAP financial information is provided above.   FORWARD-LOOKING STATEMENTS Certain statements in this update, including statements in the “Outlook” section; statements relating to the future development, production capacity and output rates, demand and market growth, deliveries, deployment, safety, range and other features and improvements, and timing of existing and future Tesla products and technologies such as Model 3, Model Y, Cybertruck, Tesla Semi, Roadster, Autopilot and Full Self Driving, our energy products and services such as Megapack, Solar Roof and Powerwall, and the battery cells we are developing and related technologies; statements regarding operating margin, spending and liquidity targets; statements regarding manufacturing and procurement improvements, cost reductions and efficiencies; statements regarding construction, expansion, improvements and/or ramp at the Tesla Factory, Gigafactory Shanghai, Gigafactory Berlin and Gigafactory Texas; and statements regarding our hiring targets are “forward-looking statements” that are subject to risks and uncertainties. These forward-looking statements are based on management’s current expectations, and as a result of certain risks and uncertainties, actual results may differ materially from those projected. The following important factors, without limitation, could cause actual results to differ materially from those in the forward-looking statements: uncertainties in future macroeconomic and regulatory conditions arising from the current global pandemic; the risk of delays in launching and manufacturing our products and features cost-effectively; our ability to grow our sales, delivery, installation, servicing and charging capabilities and effectively manage this growth; consumers’ willingness to adopt electric vehicles generally and our vehicles specifically; the ability of suppliers to deliver components according to schedules, prices, quality and volumes acceptable to us, and our ability to manage such components effectively; any issues with lithium-ion cells or other components manufactured at Gigafactory Nevada; our ability to build and ramp Gigafactory Shanghai, Gigafactory Berlin and Gigafactory Texas in accordance with our plans; our ability to procure supply of battery cells, including through our own manufacturing; risks relating to international expansion; any failures by Tesla products to perform as expected or if product recalls occur; the risk of product liability claims; competition in the automotive and energy product markets; our ability to maintain public credibility and confidence in our long-term business prospects; our ability to manage risks relating to our various product financing programs; the unavailability, reduction or elimination of government and economic incentives for electric vehicles and energy products; our ability to attract and retain key employees and qualified personnel and ramp our installation teams; our ability to maintain the security of our information and production and product systems; our compliance with various regulations and laws applicable to our operations and products, which may evolve from time to time; risks relating to our indebtedness and financing strategies; and adverse foreign exchange movements. More information on potential factors that could affect our financial results is included from time to time in our Securities and Exchange Commission filings and reports, including the risks identified under the section captioned “Risk Factors” in our quarterly report on Form 10-Q filed with the SEC on July 28, 2020. Tesla disclaims any obligation to update information contained in these forward-looking statements whether as a result of new information, future events, or otherwise. 28

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