Wednesday, September 21, 2022

Designing food environment to improve behavior

Breaking a bad habit takes a lot of willpower. The environment - family, significant others, friends, coworkers, classmates, neighbors, personal physician, religiosity/spirituality, media - can provide support or serve as a barrier to changing behavior.  

The eating environment is central to one's health since it can influence not only individual's weight—for good or for bad, but also put them at risk for the development of many diseases and conditions, such as arthritis, diabetes, and heart disease. 

Recent review analyzed 357 studies focused on the digital and physical food environments. The dimensions studied were (1) Food availability, (2)
Food prices; (3) Vendor and product properties (in terms of nutritional composition, overall quality, level of processing, etc., (4) Marketing and regulation; (5) Food accessibility (referring to individual access in terms of physical distance to shops, time, modes of transportation and daily mobility); (6) Affordability; (7) Convenience and, finally, (8) Desirability. 


Most studies focused on this topic, followed by research on vendors and their marketing strategies (especially unhealthy food marketing to children), although cumulatively there was more emphasis on external than on personal domain.

One systematic review (Rounsefell et al, 2020) indicated that digital food environment was, indeed, influencing eating patterns - for example, when peers or celebrities posted idealized images). Food-related posts on social media also influenced satiety, by amplifying feelings of hunger and neglect of satiety clues. Digitalization has the potential to increase food availability and may even provide less expensive options to specific products but is still adding a new cost to buying food, in the form of delivery fees. 

Public health interventions achieve medium-sized influences on food behavior in children and almost negligible in adolescents. Mobile apps might be effective, but the most effective behavior change technique is yet to be found. Goal setting, problem solving, periodic goal/outcome reviews and feedback, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal could all be effective to some extent. But the use of smartphone weight loss apps is still not sufficient to produce clinically meaningful health outcomes.

Behavioral interventions encourage people to act, but the actions are controlled by the individual. ~25% of individuals are influenced by existing wearable and mobile app solutions, but 75% need something better. Personalized combination of interventions, individual psychology and activity environment along with a better integration of human element are needed for designing successful digital interventions to improve health-related behavior. 


REFERENCES

Vargas‐Garcia EJ, Evans CE, Prestwich A, Sykes‐Muskett BJ, Hooson J, Cade JE. Interventions to reduce consumption of sugar‐sweetened beverages or increase water intake: evidence from a systematic review and meta‐analysis. Obesity Reviews. 2017 Nov;18(11):1350-63.

Rounsefell K, Gibson S, McLean S, Blair M, Molenaar A, Brennan L, Truby H, McCaffrey TA. Social media, body image and food choices in healthy young adults: A mixed methods systematic review. Nutrition & Dietetics. 2020 Feb;77(1):19-40.

Al Zuhaibi K, McCullough F, Salter AM. Effectiveness of health and fitness smartphone applications to improve dietary habits and physical activity in Omani adults. Proceedings of the Nutrition Society. 2017;76(OCE2).

Chew HSJ, Koh WL, Ng JSHY, Tan KK Sustainability of Weight Loss Through Smartphone Apps: Systematic Review and Meta-analysis on Anthropometric, Metabolic, and Dietary Outcomes J Med Internet Res 2022;24(9):e40141 doi: 10.2196/40141 PMID: 36129739

Tuesday, February 1, 2022

Who Benefits the Least from the COVID-19 Vaccines

Factors associated with inadequate vaccine responses in patients with breakthrough infections are still not fully understood. Studies show that genes, environment (such as air pollution), and gene-environmental interactions all influence Coronavirus disease. Less research has been done for the vaccines. 

An earlier study [Boyarsky et al, 2021]  found that 46% of transplant patients had no antibody response after two doses of messenger RNA (mRNA) vaccines. Several medical case reports about fatal breakthrough  infections listed chronic migraine, obesity, autoimmune conditionsdiabetes, atrial fibrillation, myeloma (with anti-BCMA CAR-T therapy), arterial hypertension and old age among pre-existing conditions.  Some fatal breakthroughs, however, had no apparent underlying causes. 


A new study used real-world data to evaluate risk factors of impaired antibody response to SARS-CoV-2 mRNA vaccines in individuals with chronic medical conditions evaluated in a respiratory specialty clinic. The percentage of patients without antibodies detected was as follows:

- 14% in asthma 
- 15% in COPD
- 19% in Sarcoidosis
- 36% in Interstitial lung diseases
- 37% in Rheumatic diseases
- 48% in Congestive Heart Failure (CHF). 

More than a fifth of patients with chronic medical conditions may still have insufficient levels of antibodies to fight COVID-19 even after a second mRNA vaccine dose. Interstitial lung disease and congestive heart failure are two independent risk factors for low antibody response to COVID vaccination. These patients tended to be older — between 65 and 95 years old with a median age of 80.5 — and had preexisting comorbidities, such as cardiovascular disease and Type 2 diabetes. A subset of patients was also on immunosuppressive drugs that may affect vaccine efficacy. 


Anther study that analyzed fatal breakthrough cases came with the following risk order: Overweight/Obesity; Chronic cardiac disease; Diabetes mellitus, Chronic neurologic disease; Chronic kidney disease; Chronic liver disease; Chronic pulmonary disease; Immunosupression.  Pregnancy was shown to double the risk of breakthrough infection. 

Note that the mean age of study population was 62 years and the individuals received two doses of mRNA vaccines. Newer study shows that advanced age is one of major risk factors of fatal breakthrough COVID-19 even after an additional booster dose. 


CDC data, sourced from more than two dozen states, shows that between April and June, a total of 77,000 breakthrough cases and 1,500 breakthrough deaths were recorded, compared to more than 1.74 million breakthrough cases and 15,000 deaths recorded between July and the first week of November. It is unclear exactly how many of these people had also been boosted. As of October 12, 2021, there have been at least 31,895 individuals with SARS-CoV-2 breakthrough infections who were hospitalized or died in the United States. 


REFERENCES

Boyarsky BJ, Werbel WA, Avery RK, Tobian AA, Massie AB, Segev DL, Garonzik-Wang JM. Antibody response to 2-dose SARS-CoV-2 mRNA vaccine series in solid organ transplant recipients. Jama. 2021 Jun 1;325(21):2204-6.

Juthani PV, Gupta A, Borges KA, Price CC, Lee AI, Won CH, Chun HJ. Hospitalisation among vaccine breakthrough COVID-19 infections. The Lancet Infectious Diseases. 2021 Nov 1;21(11):1485-6.

Shu-Yi Liao et al, Impaired SARS-CoV-2 mRNA vaccine antibody response in chronic medical conditions: a real-world analysis, Chest (2022). DOI: 10.1016/j.chest.2021.12.654

Saturday, January 1, 2022

Evolution of Viruses

All living beings are constantly adapting and evolving in many different ways. Genetic evolution happens because mutations - wrong building blocks of DNA - are randomly introduced during the copying and repair of genetic material. Not all mutations are meaningful, but those that affect cellular processes or lead to amino acid changes, can change the organism’s fitness - the ability to replicate and transmit and strive in different environments. Evolution can also happen through recombination. It is much faster than one nucleotide at a time and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - first identified in 2019 - is especially good in using this mechanism. 

 The original SARS-CoV-2 progressively disappeared in subsequent waves of mutated variants. 

Single nucleotide mutations started to arise and were circulating in other sequences months before new variants of concern - such as alpha - B.1.1.7 - took off, likely due to recombination events.

The Omicron - B.1.1.529  - likely picked genetic material from the common cold virus in a SARS-CoV-2 infected individual.  

How SARS-CoV-2 will evolve from here is uncertain. The current SARS-CoV-2 pandemic is fostered by asymptomatic and other types of unrecognized cases. This variety combined with mounting immunity could reduce pathogenicity. But recombination between Delta and Omicron is not out of the question and this could create a super variant.

Will the world find better ways to monitor and prevent infections? Perhaps. Scientists proposed many approaches such as testing wastewater and sampling air in public spaces. Another interesting approach is a voice analysis that could discriminate between positive COVID-19 patients, recovered COVID-19 patients and healthy individuals. Further studies will validate this and other screening technologies for effective surveillance and prevention of threats to public health.


REFERENCES

Freer G, Lai M, Quaranta P, Spezia PG, Pistello M. Evolution of viruses and the emergence of SARS-CoV-2 variants. The new Microbiologica. 2021 Dec 19;44(4).  [preprint]

Focosi D, Maggi F, Franchini M, McConnell S, Casadevall A. Analysis of Immune Escape Variants from Antibody-Based Therapeutics against COVID-19: A Systematic Review. International Journal of Molecular Sciences. 2022;23(1):29.

Robotti C, Costantini G, Saggio G, Cesarini V, Calastri A, Maiorano E, Piloni D, Perrone T, Sabatini U, Ferretti VV, Cassaniti I. Machine learning-based voice assessment for the detection of positive and recovered COVID-19 patients. Journal of Voice. 2021 Nov 26.